System for physical-virtual environment fusion

ABSTRACT

A semantic augmentation system includes a sensor with a computing system and a memory in communication with the computing system, the memory storing a plurality of endpoints. The computing system is configured to infer a first and a second semantic identity for an object, based on inputs from the sensor, project a coherent narrative and perform semantic augmentation towards a user. In further examples, the system infers a first narrative comprising two semantic identities and a second narrative wherein the system infers that a user observing view didn&#39;t infer the second semantic identity and further doesn&#39;t use the second semantic identity in the second narrative. It further, uses the corresponding narrative to remind the user to carry an item and/or credential in order to start an activity.

FIELD OF THE INVENTION

This invention relates generally to robotic devices, includingcommunicatively coupled devices which use variable semantic coherentinferences to allow the devices to perform semantic augmentation

BACKGROUND OF THE INVENTION

There are many cases in which physical devices are used in a variety ofsettings involving groups of people and/or objects, such as in theformation of posts and lines to demark crowd control areas or permittedpathways for movement. These provide regions which may be fluid, andtend to require manpower to continually reconfigure them. The poststhemselves provide opportunities forgathering/inferring/presenting/rendering/conveying information which maybe optical, visual, or otherwise. Robotic devices of this sort may servea variety of purposes in bothgathering/inferring/presenting/rendering/conveying information anddemarking areas.

SUMMARY OF THE INVENTION

A preferred robotic semantic system may include one or more smart postseach having a base (which may optionally include a plurality of wheelsor casters in the case of a mobile smart post), a power section, a trunksection, a structure fixation and manipulation portion, a controlsection, a clipping area, a portion supporting one or more antennas, andan optical sensor portion. Other modules may be incorporated with suchsmart posts including a copter module (e.g. for aerial transportation)and a display module (e.g. for providing semantic augmentation).

In one example of the invention, the smart post includes all or a subsetof the components listed above in a manner in which they are integratedinto a generally unified structure, such as a single pole or post havinga hollow center and in which the listed components are attached orinserted into the post. In other versions, the components describedabove are generally assembled separately, such that they are produced asmodules which are joined together to form the post. Thus, each of theabove sections or regions or portions may be separately formed moduleswhich are joined together, or may be separate portions of a unitary postor similar structure. In the discussion which follows, for the sake ofsimplicity each of the foregoing will be referred to as a module; itshould be understood, however, that the same description applies toother embodiments in which the module is a portion or section of thesmart post, and not necessarily a discrete module. It is to beunderstood that the post may use any number of modules of any type. Inan example, a post may comprise multiple power modules and/or multipleantenna elements modules and/or multiple cameras modules.

One example of the invention includes a semantic robotic systemcomprising a plurality of communicatively coupled devices which use aplurality of semantic routes and rules and variable semantic coherentinferences based on such routes and rules to allow the devices toperform semantic augmentation.

In some versions, the devices comprise semantic posts.

In some preferred versions, the devices comprise autonomous roboticcarriers.

In some examples of the invention, the devices comprise semanticcomposable modules.

In preferred versions of the invention, the devices comprise semanticunits.

In some versions, the semantic system includes a semantic gate.

In some examples, the semantic system comprises a semantic cyber unit.

In a preferred implementation of the invention, the semantic postsimplement crowd control.

In one example, the semantic posts implement guiding lanes.

In some examples, the semantic units perform signal conditioning.

In some versions of the invention, the signal conditioning is based onsemantic wave conditioning, preferably based on semantic gating.

In some examples, the system performs video processing.

In some examples of the invention, the system performs semanticaugmentation on video artifacts.

In preferred versions, the system may form semantic groups of posts andphysically connect them through physical movement of the semantic postsmotor components.

Preferably, the system uses concern factors in order to determinecoherent inferences.

In some examples, the system forms a semantic group based on semanticresonance.

Preferably, the system invalidates a semantic group based on semanticdecoherence.

In some examples, the system performs semantic learning based on theinference of semantic resonance.

In some versions, the system performs semantic learning based on theinference of semantic decoherence.

Preferably, the system learns semantic rules based on semanticresonance.

In preferred versions, the system learns damping factor rules.Preferably, the system learns semantic gating rules.

In some examples, the system learns a hysteresis factor based onsemantic analysis.

In preferred versions, the system performs semantic augmentation using avariety of augmentation modalities.

In some examples, the system performs semantic augmentation comprisingsemantic displaying. Preferably, the system performs semanticaugmentation on particular devices based on ad-hoc semantic coupling.

In some examples, the system performs semantic augmentation based onchallenges and/or inputs.

In some examples, the system performs semantic encryption.

In some examples, the system performs semantic gating based on semanticinferences related to at least one video frame.

In preferred versions, the system uses semantic groups to form compositecarriers.

In some examples, the devices comprise semantic meshes.

In some cases, the devices comprise biological sensors. In preferredexamples, the biological sensors comprise at least one medical imagingsensor.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred and alternative examples of the present invention aredescribed in detail below with reference to the following drawings:

FIG. 1 is a front perspective view of a preferred smart post.

FIG. 2A is a front perspective view of a preferred optical module withdome for a preferred smart post.

FIG. 2B is a front perspective view of an alternate optical module for apreferred smart post.

FIG. 3 is a front perspective view of a preferred module withmulti-array antenna elements for a preferred smart post.

FIG. 4 is a front perspective view of a preferred clipping module for apreferred smart post.

FIG. 5A is a front perspective view of an alternate clipping module fora preferred smart post.

FIG. 5B is a front perspective view of another alternate clipping modulefor a preferred smart post.

FIG. 5C is a front perspective view of another alternate clipping modulefor a preferred smart post.

FIG. 6A is a bottom plan view of a preferred standing and moving base.

FIG. 6B is a bottom plan view of an alternate preferred standing andmoving base.

FIG. 6C is a bottom plan view of another alternate preferred standingand moving base.

FIG. 7 is a front perspective view of a preferred module having acentral post.

FIG. 8A shows a representative view of a plurality of posts arranged ina guiding configuration, shown in a retracted position.

FIG. 8B shows a representative view of the posts of FIG. 8A, shownpartially extended to form a guiding arrangement.

FIG. 8C shows a representative view of the posts of FIG. 8A, shown fullyextended in one of many possible guiding arrangements.

FIG. 9 shows a plurality of posts in a perimeter delimitationconfiguration.

FIG. 10A illustrates a plurality of posts in communication wirelesslywith a remote control infrastructure.

FIG. 10B illustrates a plurality of posts in wireless communication withone another.

FIG. 11 illustrates an example of a configuration of a plurality ofsmart posts forming a configuration of smart carriers.

FIG. 12 illustrates an alternate example of a configuration of aplurality of smart posts forming a configuration of smart carriers.

FIG. 13 illustrates a plurality of smart posts, such as those in FIG. 11or 12 , but in which the telescopic capabilities of the posts defineenclosed areas within a pair of composed post structures.

FIG. 14 shows nine posts arranged in a 3×3 configuration forming acombined sensing and/or processing capability.

FIG. 15 is a representative view illustrating a combination of modules Athrough n which may combine to form a smart post.

FIG. 16 illustrates pluralities of smart posts or similar elements shownconnected via semantic fluxes.

FIG. 17 illustrates a representative map of locations and intersectionsof the trajectories of actual and semantic movement between nodes.

FIG. 18 illustrates an alternate representative map of locations andintersections of the trajectories of actual and semantic movementbetween nodes.

FIG. 19A illustrates a preferred circuit diagram for conditioning areceived signal based on a modulated semantic wave signal.

FIG. 19B illustrates a preferred circuit diagram for conditioning areceived signal based on a modulated semantic wave signal.

FIG. 19C illustrates a preferred circuit diagram for conditioning areceived signal based on a modulated semantic wave signal.

FIG. 20 illustrates a block diagram of a plurality of elements (e.g.semantic units) coupled through a plurality of links/semantic fluxes.

FIG. 21 illustrates a block diagram of a plurality of semantic unitsjoined through a multiplexer as a semantic group.

FIG. 22 illustrates a block diagram of a plurality of semantic cellsjoined through a multiplexer as a semantic group of semantic cells.

FIG. 23 illustrates a multi-stage block diagram for processing of acollection of semantic cells.

FIG. 24A illustrates a block diagram of a preferred system forimplementing a mathematical (co)processor to process the mathematicalfunctions embedded in the formulas defining semantic rules.

FIG. 24B illustrates an alternate block diagram of a preferred systemfor implementing a mathematical (co)processor to process themathematical functions embedded in the formulas defining semantic rules.

FIG. 24C illustrates an alternate block diagram of a preferred systemfor implementing a mathematical (co)processor to process themathematical functions embedded in the formulas defining semantic rules.

FIG. 24D illustrates an alternate block diagram of a preferred systemfor implementing a mathematical (co)processor to process themathematical functions embedded in the formulas defining semantic rules.

FIG. 25 is a block diagram of a semantic system including a plurality ofrobotic devices and an insurance provider.

FIG. 26A is an illustration of an observer directing attention to afirst endpoint within a semantic field of view.

FIG. 26B is an illustration of an observer directing attention to asecond endpoint within a semantic field of view.

FIG. 27 is an illustration of a field of view mapped to a displaysurface.

FIG. 28 is an illustration of a field of view mapped to an alternatedisplay surface.

FIG. 29 is an illustration of a field of view mapped to an alternatedisplay surface.

FIG. 30 is an illustration of a field of view mapped to an alternatedisplay surface.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention relates to versatile smart sensing robotic posts,appliances and systems. Such systems can be used in various environmentsincluding airports, hospitals, transportation, infrastructure works,automotive, sport venues, intelligent homes and any other circumstances.In one version, the posts serve as stanchions and include clips orconnectors for belts or ropes which may optionally be retractable withinone or more of the posts. In this form, the smart posts may be used asbarricades or crowd control in areas where it is desired to restrict ororganize access to certain areas by a population.

In further use cases the smart posts may be used as appliances and smartinfrastructure for applications such as robotics, wirelesscommunications, security, transportation systems, scouting, patrollingetc.

The system may perform semantic augmentation, wherein the system usessemantic analysis for inferring/presenting/rendering/conveying/gatheringinformation in optimal ways and/or using particular modalities based oncircumstances, challenges, users and/or profiles.

In further application the smart posts are used for semanticaugmentation via incorporated displays, speakers, actuation and otherI/O mechanisms. In some examples, a display is mounted on the postand/or top of the post.

In further examples, the smart posts may comprise smart pop-up signswhich allow traffic control (e.g. REDUCED SPEED, CONTROLLED SPEED etc.).Alternatively, or in addition, the posts may comprise other semanticaugmentation capabilities and/or outputs. It is to be understood thatthe signs/posts may register their capability semantics on the semanticsystem and the system controls them based on semantic augmentationand/or analysis including semantic time management (e.g. REDUCED SPEEDUNTIL ACCIDENT CLEARS, CONTROLLED SPEED UNTIL TRAFFIC FLOW IS NORMALetc.).

The preferred smart posts (or appliances) may move independently or maybe installed on moving vehicles and any other moving structures;alternatively, or in addition they may be installed on fixed structuressuch as walls, floors, and so on for sensing and control purposes.

Typically, a preferred post has sensing elements including at least avision element such as a camera, and an array of antenna elementsreceiving and/or radiating electromagnetic radiation. Theelectromagnetic radiation may use various frequency spectrums includingbut not limited to low frequency, ultra-high frequency, microwave,terahertz, optical and so on. The camera and/or vision element mayoperate in visual, infrared and any other optical spectrum. It is to beunderstood that sensing elements may provide time of flight (TOF)capabilities.

In addition to electromagnetic energy sensing the smart robotic postsmay include other sensing modalities (e.g. microphones) and/or any otheranalog and/or digital sensors and transducers used for otherenvironmental measurements and detections (e.g. pressure, sound,temperature, motion, acceleration, orientation, velocity etc.). It is tobe understood that such elements may be disposed in an arrangement aboutthe smart post to enable detection of environmental conditions orparameters in geographic areas or zones about the post.

The system may use environment profiling and learning based oncorroborating radiofrequency energy returns with optical (e.g. camera)sensing wherein both modalities sense conditions in the semantic model(e.g. at various endpoints) and create semantic artifacts (e.g. semanticgroups, semantic routes) based on sensed conditions and semanticanalysis. In an example the system determines artifacts through cameraframe sensing and/or inference operating in optical spectrum and groupsthem with artifacts sensed and/or inferred through antennas operating inthe microwave spectrum. Thus, the system may be very particular onconditions and inferences that resemble learning groups and patterns.

As depicted in FIG. 1 a preferred smart post 101 comprises a base 1(which may optionally include a plurality of wheels or casters 10 in thecase of a mobile smart post), a power section 2, a trunk section 3, astructure fixation and manipulation portion 4, a control section 5, aclipping area 6, a portion supporting one or more antennas 7, and anoptical sensor portion 8. While the illustrated embodiment shows ahexagonal design (as viewed in a horizontal cross section taken througha vertical axis, in which the vertical axis extends centrally from thebase to the optical sensor portion) it is to be understood that it canbe shaped differently (squared, pentagonal, octagonal, circular etc. inother versions. Also, other modules may be incorporated with such smartposts including a copter module (e.g. for aerial transportation) and adisplay module (e.g. for providing semantic augmentation).

In one example of the invention, the smart post includes all or a subsetof the components listed above and illustrated in FIG. 1 in a manner inwhich they are integrated into a generally unified structure, such as asingle pole or post having a hollow center and in which the listedcomponents are attached or inserted into the post. In other versions,the components described above are generally assembled separately, suchthat they are produced as modules which are joined together to form thepost. Thus, each of the above sections or regions or portions may beseparately formed modules which are joined together, or may be separateportions of a unitary post or similar structure. In the discussion whichfollows, for the sake of simplicity each of the foregoing will bereferred to as a module; it should be understood, however, that the samedescription applies to other embodiments in which the module is aportion or section of the smart post, and not necessarily a discretemodule. It is to be understood that the post may use any number ofmodules of any type. In an example, a post may comprise multiple powermodules and/or multiple antenna elements modules and/or multiple camerasmodules.

The base 1 may comprise wheels 10 and its movement be controlled viamotors, actuators and other control components or interfaces by acomputer (or the equivalent, such as a processor having a memory andprogramming instructions) embedded in the robotic post. The standingbase may comprise suspension (e.g. springs, shock absorbers, coils,coil-overs, piezo components etc.) and attachment mechanisms for wheelsor for attaching to a structure (e.g. automobile).

FIGS. 6A-C illustrate bottom plan views of the standing and moving base1 in various embodiments comprising attaching mechanisms 20 and/ordriving wheels 21. The (driving) wheel or wheels may mount on attachingmechanisms and/or be retractable, tension-able and/or spring-able (e.g.for using, holding and releasing energy for achieving particularcompressions, extensions and/or motions); in an example, the post mayuse any three wheels, each on any non-adjoining edge/segment of thehexagonal shaped base while the other wheels may be inactivated and/orretracted. Analogously the driving wheels may function on similarprinciples (e.g. activate particular ones based on (semantic)circumstances and/or semantic groups). Further, the mounts (wheelmounts, ball type mounts, module connecting mounts, band connectingmounts etc.) may be controlled (e.g. by compression, extension etc.) bysemantic actuation based on observed circumstances. In an example, somemounts' compression is stiffened and others loosened when the systemuses, observes and/or infers a trajectory which would determine an 80HARD LEFT LEAN semantic; further, the 80 HARD LEFT LEAN may use furtherroutes such as WHEEL MOUNT GROUP LEFT 75 COMPRESSION, WHEEL MOUNT GROUPRIGHT 25 COMPRESSION.

In further examples, at least two post rectangular bases comprise eachfour wheels in a rectangular pattern one for each edge; when joined onone of the lateral edge faces the base allows a combined support andthus the center of gravity moves towards the joining edge face. Insteadof using the combined eight wheels for movement the combined post mayuse any inferred particular group from the combined base (e.g. in atriangular pattern, rectangular pattern etc.) and thus adapting toconditions, movements and efficiency.

Each module may comprise a computer or controller, memory or othercomputing units. While illustrated as separate modules, in otherversions one or more physical modules and/or their functionality mayfuse or be distributed among fused modules. For example, the standingbase and moving module 1 may be fitted with a power supply such as oneor more Li-Ion batteries, and therefore may serve as a singleconsolidated base and power supply module rather than two separatemodules. In other embodiments, the power, control and antenna elementsare combined in a single module rather than separate modules joinedtogether. In yet other embodiments the trunk and antenna panels extendto the whole surface of the post.

The power module may comprise batteries (e.g. Li-Ion), fuel cells, supercapacitors and/or other energy storage components. The electricalstorage components may be charged via physical plug-in, wireless or anyother charging technique.

As explained, multiple modules, whether physical or logical may fuseinto a larger trunk module. In some examples such fused trunk module istelescopic and extensible, facilitating dynamic reconfigurationsettings.

In some embodiments the standing base module and the trunk module aretelescopic thus allowing height adjustment. The telescopic movement maybe controlled through electric motors powered through the power moduleand controlled by the control module.

In some versions, the modules may be carried on a supporting post orframe, which may be configured as a central post defining a centralvertical axis for the smart post. The modules may be attached to thepost 9, as shown in FIG. 7 , through a variety of mechanism with thepreferred version being that the post comprises a frame on which modulesslide, attach and lock/unlock (e.g. FIG. 7 middle column 9). In someversions the supporting post or frame comprises backplanes, connectorsand/or communication buses; when slide into place the modules connect(e.g. via connectors) to the backplane, connection and/or communicationbus, thus allowing flexible module interconnects (e.g. FIG. 15 , showinga plurality of modules which includes Module A, Module B, and continuingthrough Module n).

Alternatively, or in addition, in other embodiments the modules compriseinterlocking and interconnect features such as tongues and grooves, pegsand cavities, tabs and slots and/or other interconnect systems thatallow the modules to lock to each other while being stacked.Interconnect mechanisms allow the modules to be in signal communicationvia a composable bus formed by interconnecting buses of each module. Itis to be understood that the buses may comprise electrical and/oroptical components.

In some embodiments a collection of any types of modules may alsocommunicate wirelessly via transmit/receive components, antennas and/orpanels embedded in each module. In some embodiments the communicationbetween modules take place in the same post and/or other posts.

The modules may be in signal communication and communicably coupled forvarious purposes including for transmit/receives command signals viabuses, providing status information (e.g. battery charging status),semantic augmentation (e.g. airline name, flight information, routinginformation etc.) and so forth. Post to post communication may alsooccur in such situations and further when the system infers, groupsand/or deploy posts and units in particular configurations and/ormissions.

In an example, the control module provides commands to actuatorsincorporated in the base module for guiding the posts throughenvironment. Further in the example the control module may infersemantic routes such as GO TO LOCATION A and further TURN LEFT UNTIL ONTHE DIRECTION OF LOCATION A and further when detecting a curb MODERATELYACCELERATE TO CURB AND JUMP. The system may further infer from JUMP andHIGH CURB to LOAD SPRING 1 HIGH (e.g. commanding driveline suspensionspring 1 to load high tension via electrical motor actuation) andRELEASE SPRING 10 (e.g. high energy release) once HIGH CURB CLOSE. Asmentioned, the control units command actuation based on such commands(e.g. commands electrical motors of the base module driveline, controlsvoltages, currents and/or electromagnetic fluxes/properties in time ofsuch components etc.). While the previous example has been referred tocommunications between modules of the same post it is to be understoodthat similar use cases for post units and/or groups may require interpost communication and command whether master-master and/ormaster-slave.

In some examples the carriers command semantic groups of posts and/ormodules in order to achieve particular movements. In an example, acomposite 3×3 carrier may need to climb a stair and as such it maycommand rows of posts independently at particular times for achievingthe goals.

The system elevates at least the first row of posts from the ground oncein proximity of a stair and further moves forward and elevates furtherrows in order to climb the stairs while always maintaining the loadinitial posture (e.g. horizontal agnostic).

In an example of a climbing system the robotic system may be consideredas formed from a number of rows and columns rows and columns and groupsthereof. Thus, when climbing a stair at least the front upper row ofmodules moves upward (e.g. via telescopic means) and slide forward andrests at a first time on at least the second stair up from the currentposition. Once in position the lower level horizontal rows move inposition forward on the subsequent stairs under the upper row position'sstairs and generate telescopic lift for the upper level horizontal rowsthat will detach from the upper stair/s, slide up and forward to attachto higher upper stairs and generate support for the ensemble allowingthe lower level rows to detach from the supporting position and slide upand forward to upper stairs. While from the horizontal rows point ofview stairs ascent is based on row movement such as slide up andforward, from the vertical columns point of view the movement istelescopic and/or retractable to elevate the horizontal rows.Analogously with stair ascent, stair descent is based on moving thevertical columns in a slide forward and down movement while thehorizontal rows use a telescopic and/or retractable movement to slideforward the vertical columns. It is to be understood that in some casesthe carrier may turn over on one side (e.g. such a vertical row becomehorizontal and vice-versa) and/or reconfigure its layout for theparticular mission (e.g. ASCENT, DESCENT etc.).

While in the example we may have referred to “row” and/or “column” it isto be understood that they may be used interchangeably with “semanticgroup of rows” and/or “semantic group of columns” and further, in ahierarchical manner, of semantic groups. The selection of rows and/orcolumns of sliding, telescoping, retracting and/or lifting elements maybe based on semantic group inferencing which may also take inconsideration the lift weight and height (e.g. weight of carrier andload, height of load, height of telescoping areas, height of stairsetc.). Other factors such as surface traction grip, environmentconditions and other factors may also come into effect.

In other examples, the semantic posts may use group leverage to achievegoals such as changing positions, lifting, jumping, getting straightand/or out of the ground. In an example, at least one post is sidewayson the ground (maybe because it was pushed to the ground by externalfactors) and other posts are used to lift the fallen post and move itback to vertical position. In further examples at least two posts havefallen, and they leverage each other to lift to vertical position basedon side by side maneuvering, latching, hooking, lifting, pushing and/orpulling.

It is to be understood that in some cases the post deployments based onsemantic routes may be based on the semantics associated with variouslocations and/or other information. In an example the system detectsthat the area of GATE A having a scheduled DREAMLINE AIRLINE flight isDELAYED or boards later and hence smart posts at the gate may bere-deployed to other locations and areas based for example on areward-based system. In such a system, the posts are deployed tolocations associated with semantics having high rewards and incentiveswhile pondering the total rewards (e.g. via opposite sign weights and/orrewards) with the accessibility, deployment and routing semantics in thesemantic network model. In an example, the system infers a goal ofredeploying the posts to a HAZARDOUS area (e.g. area B and/or viaendpoint associated with B) which may entail high rewards in aparticular circumstance however, routes and/or accessibility to the areaare not available immediately (or maybe too busy) and/or maybe powerscarcely available and thus increasing risk and/or lowering the totalrewards of evaluating pursuing the goal via location endpoint B. Inaddition, the semantic inference allows goals, rewards and/or semanticroutes to be adjusted and/or selected based on further semantic routes,goals and/or rewards (e.g. MINIMIZE COST AND RISK, MOVE FAST, MAXIMIZEPOWER CHARGING etc.). It is to be understood that the semantic routesand/or goals may be hierarchical and compositional with higher-levelabstraction semantic routes and/or goals comprising lower-levelabstraction semantic routes and/or goals in a hierarchical and/orcompositional fashion. Such hierarchy may be determined and/or mapped tohierarchies and topologies in hierarchical semantic network models thusallowing the semantic inference to pursue selectively (e.g. based onhigher level endpoints comprising a lower level sub-model comprising aselection of endpoints and/or links) and hierarchically from lower tohigher and higher to lower abstraction (e.g. endpoint) levels.

While in the previous examples a rewards-based system has beenexemplified, it is to be understood that analogously other factors andindicators may be used for inferring, setting and/or evaluating semanticroutes and/or goals (e.g. based on risk, cost). Further, such factorsand indicators may influence one another via semantic inference (e.g. 10RISK infers HIGH COST, HIGH COST infers HIGH RISK, HIGH RISK infers HIGHPAY REWARD, high reward goals infer high risk routes etc.).

The system may perform semantic factorization wherein a quantifiable(semantic) factor/indicator associated with a semantic artifact isadjusted based on semantic inference/analysis. It is understood thatwhen referring to “factorization” in this disclosure it may refer to“semantic factorization”. Semantic factorization techniques may be usedsuch as explained in this application (e.g. based on semantic timemanagement, decaying, indexing, resonance, (entanglement) entropy,divergence, damping etc.).

Semantic factorization may entail semantic decaying.

Semantic decaying occurs when a quantifiable factor/indicator associatedwith a semantic artifact decays or varies in time, most of the timetending to 0; as such, if the parameter is negative decaying isassociated with increases in the semantic factor value and if the factoris positive decaying is associated with decreases in factor's value.Sometimes, when the semantic decays completely (e.g. associate factor is0) the semantic may be inactivated, invalidated or disposed and notconsidered for being assigned to an artifact, semantic route, goal,semantic rule, semantic model and/or inference; further, based on thesame principles the semantic is used in semantic group inference andmembership.

Semantic factors may be associated with values of control voltages andcurrents in analog and/or digital components and blocks. Analogously,other material and further emission, dispersive, diffusive and/orquantum properties may be controlled (e.g. electromagnetic flux,conductivity, photon/photoelectron emission, polarization, etc.).

Decaying and semantic factors may be inferred and learned with semanticanalysis. In some examples the system learns decaying and semanticfactors for semantic rules and/or semantic routes.

The clipping module 6 (see FIG. 4 ) comprises bands and clips that canbe used to hook up or pair two posts, such as by the attachment ofopposite ends of a band, rope or belt to two separate posts. Each clipmodule has at least one band (see FIG. 4 showing one end of a bandhaving a clip 25 attached, in which the band is retracted within themodule) such that the attached clip or hook that can be used to cliptogether at least two posts by joining to a band clip insert orattachment point 26 on another post. The bands can therefore be extendedto form a perimeter by moving and guiding the posts to the desiredlocation. Once coupled or hooked the posts may move, thus extending theclipped bands and creating various configurations, potentiallydelimitating semantic zones (e.g. traveler or automotive guiding lanes,hazards emergency lanes, parking areas/lanes/space, work zones etc.). Itis to be understood that while bands are exemplified for simplicity,other types of physical couplings may be used such as foldable barriers,nets etc. Alternatively, or in addition to the physical couplings theposts system may be performing the access control and/or zoning functionvia physical movement and/or sensing means (e.g. laser, vision,radiofrequency and/or other modalities).

Analogously, when the posts need detaching, they may move towards eachother in order to detach the band clips at a closer distance in order toavoid band dangling. In other examples the posts detach while at fartherdistances and the band rolls attenuate the retraction movement throughamortization or controlled retraction (e.g. based on springs and/orelectrical means). It is to be understood that the semantic posts mayperform clipping/unclipping, unfolding/folding of the bands, barricadesand/or nets once they are commanded to allow/deny/control access.

In some examples, the posts may not move to each other in order toperform clipping but rather perform the shooting of drive threads, ropesand/or cables towards each other that may hook once colliding in the air(e.g. male-female type of hooking, where one thread is a male connectorand the other thread is a female connector). Once disconnecting suchthreads, ropes and/or cables may have mechanisms to manipulate the endhooks and latches.

FIGS. 5A-C show further exemplary preferred embodiments for couplingmechanisms to affix belts or bands from one post to another post. Thecoupling mechanism between two clips or hooks may comprise a slidingmechanism 31, insertion lock mechanism 32, hook lock mechanism 33,turning mechanism, plug and lock mechanism, latching an any othertechniques. The sliding mechanism comprises hooks, clips or grooves thatslide into each other via horizontal or vertical movement. The plug andlock mechanism may comprise plugs that lock into each other onceconnected. In a similar way the latching mechanism latches the hooksonce connected. It is to be understood that any of these techniques usemechanical and/or electrical means for such clippings and latches andcan be combined in any configuration.

The semantic posts may comprise a (foldable) barrier mechanisms and/ormodules. The barrier mechanism/module may comprise/control multiplebarrier segments (e.g. from plastic, metal, fabric and/or any othermaterial) which can be folded and/or extended thus forming shorter orlonger barriers used to adapt to (semantic) access control needs (e.g.entry points, controlled areas/endpoints etc.). Such barriers maycomprise segments with grooves which slide, extend and/or retract withineach other with the sliding movement being controlled via(electro)magnets, toothed rails, strings and/or cables. The barriermechanism/module allows the barrier to lift/raise or drop based onsemantic access control. It is to be understood that the barriersegments may be folded and/or stowed thus shortening the barrier to aparticular/minimum size. Further, the barrier may be stowed along thevertical length of the posts; further, the (compacted) barrier may slidedown along the vertical side of the post and thus adjusting the heightof the post to an optimal/minimum height.

The barriers from at least two semantic posts may join and/or locktogether using joining and/or locking mechanisms; such mechanisms maycomprise mechanical and/or magnetic components. In some examples, thetips of the barriers comprise magnets which when in vicinity attract andlock together. Magnetism in the components may be controlled by semanticunits (e.g. via a voltage, current, inductance, magnetic flux etc.) andthus controlling the timing (e.g. by time management) and/or intensityof the attracting and/or repelling magnetic fields.

Two joining posts may use joining capability for communication,networking and/or energy transfer. In some examples, the bands, clips,barriers and their latches/connections/tips incorporate feed cables andconnections.

It is to be understood that while in some examples the posts comprisecapabilities such as joining and/or delimiting bands, barriers, pop-upsigns and so forth in other examples they may lack such capabilities.

The semantic zoning and access control may be implemented by physicalmoving and positioning of the posts (e.g. as blocking posts, delimitingposts, guiding posts, semantic zoning posts etc.). In some examples theposts may or may not comprise joining and/or delimiting elements.

The semantic zoning and/or access control can be based on theaugmentation provided via pop-up signs (e.g. capabilities, rise/fallcommands etc.), displays (modules) attached to the semantic posts and/orother semantic fluxes.

The semantic posts may be controlled via a centralized and/ordistributed computer system where the functionality is distributed amongpluralities of control modules and/or other external computers, computerbanks or clouds. In some examples the distributed computer system isorganized in a hierarchical manner.

The power module may comprise a power hooking mechanism that is used toplug-in and recharge the power module. It is to be understood that theplug-in may be automatic based on sensing and robotic capabilities. Inan example, the charge socket is localized via sensing and the systemguides a post's rechargeable plug via orientation and/or routing in asemantic network model where at least one endpoint is mapped to thelocation of the charge socket; further, at lower endpoint levels otherlocation based features and/or shapes of the socket are mapped and usedwith orientation and routing. It is to be understood that the locationof the charge socket may be mapped and detected via any availablesensing technique or a combination of those. In some examples, shapes,sockets and/or its features are detected via camera sensing (e.g. frameprocessing based on deep learning, semantic segmentation, semanticanalysis etc.). Further, the power module can be attached or detached bysliding and/or lifting the assembly (e.g. other modules, trunk) on topof it, potentially using the attached hooks and further lifting thepower module and replacing it with another one.

The structure fixation and manipulation module 4 is used to attach thesmart post to various fixed and mobile structures including walls andbases in any orientation. In some examples the base is a structure of acar, drone, aircraft or any other mobile structures. In similar wayswith the clipping the fixation module it may incorporate variouslatching, hooking and clipping mechanisms for attachment that may bepresent sideways and/or underneath. Further, the latching and lockingmechanism may allow the movement and orientation of posts in variousangles.

In some embodiments the clipping module and/or the structure fixationand manipulation module are used to compose larger formations and/orstructures of smart posts. In some examples, those formations are basedon semantic inference and semantic groups of posts. In an example, agroup of smart semantic posts are joined together to form a largerstructure (e.g. a larger transportation system, trailer unit, bed truck,vehicle, drone etc.). It is to be understood that the composablestructure can comprise a variety of configurations of the smart posts;for example there may be posts in the structure comprising sensing unitssuch as optical module and/or antenna elements module while other postsin the structure (e.g. used to compose a flat transportation bed) maynot have such capabilities (e.g. comprise a combination of the movingbase module, power module, clipping and fixation module, control moduleand/or trunk module including any telescopic capabilities). FIGS. 11 and12 present example of such configurations where smart posts (forexample, posts 101 a through 101 e; for simplicity, not all posts shownin FIG. 11 or 12 are labeled) are used in conjunction to form variousconfigurations of smart carriers. As shown in those examples the systemcomposes the sensing able posts with reduced posts (lacking some sensingcapabilities) in order to form smart flat carrier beds.

Such composable configurations may be based on goals, missions andrewards thus, the system selecting the optimal configuration. In furtherexamples, mission collaboration may occur where goals and/or sub-goalsare split, challenged and/or distributed between modules, posts and/orsemantic fluxes by semantic leadership.

In a similar manner of posts structure composability other smartcarriers, hunters or formations may be achieved. In an example a groupof posts are used to hook up and carry a net (e.g. for droneneutralization goals and purposes). In other examples, a group of postshook up and carry drone neutralization measures (e.g. arrow launchers,high powered lasers, mini-drones etc.). In some examples the systemdeems an area as needed to be cleaned up of drones and based on the goalthe system launches ANTI DRONE and DRONE DESTROY missions and routes.Such missions may be inferred for example based on user or flux feedbackand/or input (e.g. mark an area, endpoint and/or trajectory as CLEAN OFDRONES IN 20 MINUTES etc.). It is to be understood that those missionstake in consideration the chain of authorization and/or hierarchy (e.g.of users and/or fluxes) in order to avoid potential conflicts. In anexample, an area-based endpoint EC encompasses area-based locations EAand EB. When semantics and missions from a higher-level authorization ismarked and/or established for such areas they will take leadership overlower authorization levels; the system pursues goal based inference onsuch missions with leadership associated to higher level authorizationsemantics, missions and groups; in the case of increased superposition(e.g. potentially based on a entropy and/or superposition indicator,factor, rate and/or budgets) the system may perform superpositionreduction by asking for additional feedback (e.g. from a user, identityor semantic group based on authorization level, flux etc.) and/orassigning additional bias based on profiles and/or preferences. If nofeedback or profile is available, the system may perform the missionsbased on higher levels policies and/or hard route semantic artifacts. Itis to be understood that the authorization levels may be inferred forvarious semantic identities, semantic groups and/or semantic profilesbased on semantic analysis and leadership. Thus, in a first context(e.g. as determined by a semantic view, route etc.) a semantic group Amight be assigned a higher authorization level than semantic group Bwhile in a second context the group A might be assigned a lowerauthorization level. In addition, or alternatively, the authorizationlevels (access control) are assigned based on inferred semanticartifacts (e.g. semantic routes, semantic profiles etc.) and the systemuses the semantic artifacts and further projections for furtherinference and validation of authenticity.

A confusion semantic factor may be inferred based on the incoherentand/or coherent superposition factors, indicators, rate and/or budgetswherein the confusion factor is high if the incoherent superposition ishigh and/or coherent superposition is low. Analogously, the confusionfactor is low when the incoherent superposition is low and/or coherentsuperposition is high.

The system may prefer coherent semantic artifacts during analysis whenthe confusion factors are high and may use more incoherent semanticartifacts when the confusion factors are low.

Allowed confusion factors thresholds, intervals and/or budgets may beinferred, ingested, adjusted and/or predefined by inputs from users,semantic fluxes and semantic analysis. Confusion factor semanticintervals may be associated with semantic artifacts (e.g. semanticroutes and/or rules) thus allowing the system to apply such artifactswhen the system exhibit a particular confusion range. In some examples,the higher the confusion factor, the higher priority based on leadershipand/or factorization have the rules that are associated with suchintervals (hard routes and rules may have explicitly or implicitly thehighest priority).

In cases where the allowed confusion is high and/or unbounded the systemmay exhibit an undetermined (time) interval of confusion and thus thesystem may use further semantic rules (e.g. access control, timemanagement rules) to restrict and/or bound the confusion interval.

The system may adjust factors, budgets and or quanta in order to controlthe inference towards goals and/or keep (goal) semantic inference withina semantic interval.

The system may infer DO NOT semantic artifacts (e.g. rules, routes etc.)associated with the semantic artifacts which generated (increase in)confusion (in semantic views).

Increases in confusion may be assessed based on thresholds, rate ofincrease, mapped overlays, indexing, hysteresis etc.

In further examples, when semantic areas intersect, overlap and/or arecontained, the system may use the semantic areas depth axis (e.g. Zaxis) attribute for hierarchy determination and for establishing theleadership semantics. In one example, if the area associated to endpointEB is specified on the Z axis on top of area associated to EC, thesystem may provide more leadership bias towards semantic artifactsassociated with higher placement on the Z axis, in this case EB. Whilethe example specifies the positive bias towards higher Z axis factors itis to be understood that such biases may be configurable or provided aspart of semantic profiles (e.g. associated with users, identities,semantic groups, semantic artifacts etc.).

It is understood that the authorization rights and levels may be basedor assigned on hierarchy levels and/or artifacts in the semantic model.For example, the right for DRONE SHUTDOWN related artifacts may beassigned to particular semantic groups (e.g. of users, semantic posts,endpoints etc.). While the previous example relates to a more specificapplication it is to be understood that the semantic network modelinference may be guided by semantic superposition factors and/or biasesprovided in the context of semantic profiles and/or authorization atvarious hierarchy levels.

In some examples two endpoints may be associated with two zones whichoverlap (e.g. by coordinates, geographically, semantically etc.; twoproperty/facility areas overlapping on a no man's land zone between twoproperties mapped to endpoints). Further, if the endpoints areassociated with semantics and narratives and the endpoints areassociated each with various semantic fluxes and/or agreements then thesystem may infer the intersection endpoint (a third endpoint) as an areaassociated with an inferred agreement (e.g. based on strongfactorization) between the two semantic fluxes and/or agreements basedon semantic analysis. Further, at least one endpoint associated and/orcomprising the first and the second (and potentially the third)endpoints and based on the reunion of those zones may be associated withthe semantics, agreements, fluxes and/or narratives of/at the twoendpoints plus additional semantics, agreements, fluxes and/ornarratives resulting from semantic analysis on such composableartifacts. Thus, the system infers and maintain hierarchical structuresof semantic artifacts which help assign the law of the land and/oragreements to various mappings. It is to be understood that law of theland and/or agreements may be composed and comprise various semanticartifacts associated and/or particularized with semantic groups,semantic identities and so forth; further semantic analysis of thecomposable laws of the land may be based on semantic groups and/orsemantic identities (e.g. TRUCK OPERATORS, NURSE/S HOLDING A NEWSPAPER,JOHN'S DE LOREAN etc.). It is to be observed that the semanticidentities (e.g. NURSE/S HOLDING A NEWSPAPER, JOHN'S DELOREAN etc.) maybe developed in time based on semantic inference and may be related withsemantic groups; further they can be inferred by semantic grouping. Inan example semantic identity of NURSE HANDS and of a NEWSPAPER areformed as a semantic dependent group. In other examples, a semantictrail/route of NURSE, (HANDS, HOLD), NEWSPAPER may be used. In caseswhere the semantic identity and/or group collapses (e.g. to oneartifact) in the inferred circumstances (e.g. as reflected based onsemantic views and semantic artifacts) the system may be more specificabout the semantic identifiers (e.g. “THE” NURSE HOLDING A NEWSPAPER,NURSE JANE, HEALTH AFFAIRS etc.). Further, the system may associate,group and/or learn semantic routes and/or rules (e.g. NURSE, HOLDING THENEWSPAPER, WEDNESDAY, AFTER LUNCH—(NURSE) JANE (99.99%); (NURSE) (JANE),HOLDING THE NEWSPAPER, WEDNESDAY AFTER LUNCH—70% etc.). Such inferredand learned artifacts may comprise time management (e.g. WEDNESDAY AFTERLUNCH); further, based on the semantic route and the identification ofJANE it may create behavioral routes for the semantic identitycomprising leadership semantics (e.g. NURSE and/or more precisely forNURSE JANE and/or JANE).

In further examples, the system detects semantic shapes which moveand/or are linked together and thus infers semantic grouping and/oridentities. There may be instances where the semantic group (semantic)and/or semantic identity are/is associated with indicators and/orfactors comprising higher confusion, low trust and/or risk (e.g. becausethey are unnatural, not learned, not believable etc.); further, the(semantic) leadership and/or factorization of one shape over the othermay determine the semantic identity. In an example, the system detects awheel and a mobile phone spinning around the wheel (e.g. in anun/controlled manner); while the factorization of the parts allowpotentially very believable inferences, the factorization of thecomposite reflects it's hard believability as does not resemble anyknown route and/or is hardly/not diffused by semantic rules.Nevertheless, the system may infer a semantic route, group, shape and/orrule which have and/or are associated with decayed believability,elevated confusion and/or high-risk indicators and/or factors. Further,based on the factorization of particular circumstances and/or profilesthe composite semantic inferences (e.g. of identities, routes,endpoints, SPINNING PHONE AROUND A WHEEL, SPINNING WHEEL WITH A PHONEetc.) may be factorized differently and have different believabilityfactors. The believability factors may be associated with particularsemantic groups and/or leaders. In the example, the system may provideleadership of the (composite) semantic artifacts which are morebelievable (e.g. SPINNING WHEEL vs SPINNING PHONE etc.). It is to beunderstood that the system may use semantic shaping and/or overlaying of(known/saved) semantic network models in order to infer suchbelievability factors and/or artifacts.

The inferences may be guided by privacy rules which may allow, denyand/or control inference and/or collapsing and thus inferring only theallowed level of granularity for semantic identities and/or semanticgroups. In some examples, privacy rules may deny inferring, projectingand/or using semantic identities associated with a particular thresholdor lesser number of objects and/or artifacts. It is understood that thelevel of inference granularity may be based on hierarchical and/orprojected inference.

The system may infer/assign leadership on particular locations,endpoints and/or semantic groups thereof to particular semanticidentities and/or semantic groups thereof. Such leadershipinference/assignment may be based for example semantic analysisincluding semantic time management. The (semantic) leadership may beinferred/assigned based on particular goals and/or factor intervals. Inan example, two entities E1 and E2 (e.g. governments, companies etc.)share a common FISHING area and are bounded by a goal/sub-goal ofDEVELOP FISHING, KEEP THE WATER CLEAN or DEVELOP FISHING BUT KEEP THERISK OF CONTAMINATING THE WATER LOW. If the goals/sub-goals are not metwhile under a particular entity leadership (e.g. E1) then the system maychange ratings of the entity E1 in rapport with the goals/sub-goals andpotentially update and/or index the time management rules asserting theleadership of the other entity (e.g. E2); thus a new leadership (E2) isinferred and exerted (e.g. based on semantic profiles of E2) once theconditions are breached while potentially bounding the breaching entity(E1) with goals (e.g. creating semantic artifacts including semanticroutes, time management rules etc.) to (help) bring/recover theconditions to an agreed semantic artifacts baseline, anchor and/orgoals. It is to be understood that such inferences, ratings and/orleaderships may be related with more complex environments with multipleentities, semantic fluxes and/or semantic groups contributing tocollaborative contractual inferences such as explained throughout theapplication.

Semantic leadership is inferred and/or adjusted based on semanticanalysis including semantic factorization.

The system uses semantic gating at endpoints in order to preserveconfidentiality in relation with semantic inference associated withinferences related to objects and/or semantic identities passing throughthe endpoints.

While the examples show the modules stacked in a specific order it is tobe understood that the order may be different in other applications. Insome embodiments the antenna module may be positioned on top of theoptical module; further, in other embodiments the optical module may notbe present at all with the optical detection capabilities beingperformed by the antenna module. While this are specific examples, thegenerality and applicability of flexible module compositions extend toany configuration. In other examples as depicted in FIG. 13 , thetelescopic capabilities of the posts may allow the realization ofenclosed areas within a composed post structure. For example, asillustrated, posts 61 are all “high raised” posts forming a perimeterabout posts 62 which are relatively lower. The “high raised posts” areusing telescopic capabilities to form an enclosed area on the lowerposts. Such areas may be used for example to store or conceal tools,articles and any other artifacts. The enclosed posts area by the highraised posts may be based on a semantic group inferred based on a sensedpressure exercised by a load on the enclosed posts.

In further example the system elevates the post (e.g. via telescopicmeans) for hooking and/or latching to person or transportation wagonsthus the composite carrier acting as a driveline for such wagons. Thus,the system may select specific wagons based on specific needs inferredvia semantic inference and analysis. In further examples, users selectspecific wagons and the system assembles carrier beds based on thecharacteristics of the wagons and potentially the characteristics of therequired route. It is to be understood that a wagon carrier drivelinemay be composed from a plurality of detached carriers and/or beds (e.g.a driveline comprises four carrier beds, one for each corner of a wagon)which may be represented and/or inferred as semantic groups.

In further examples, the system elevates posts for guiding, lockingand/or connecting other artifacts or components into the enclosed areas;in an example the system encloses a higher capacity battery of a largersize wherein the system uses goal-based inference to determine thebattery type and infer the enclosed area where to be placed. Further, inother examples the smart posts can join and/or clip for improved sensingand processing. FIG. 14 shows nine posts 101 a-i in a configuration of3×3 forming a combined sensing and/or processing capability.

In some examples, the composability of such elements and groupings isbased on specific goals that may be specified by a user and/or inferredby the system. Further, when considering the goals and missions thesystem may use rewards and other factors-based inference.

For example, such goals may comprise of CARRY 7 BIG LUGGAGES or CARRY 76 BY 6 LUGGAGES and the system estimates the size of a flatbed and thenumber of required posts to form the flatbed based on mapping endpointsto areas to be covered by posts, luggage, and/or by using its ownestimation of size, weight and/or indexing of the semantic BIG. Inaddition, the goal may comprise further restrictions such as USING AMAXIMUM 4′ CARRIER WIDTH; such restrictions may be based for example onestimating an optimal route of travel (e.g. based on a semantic route)where the system detects that particular areas and/or endpoints to betraveled comprise restrictions (e.g. a location comprising a door of 4′width). Thus, in some examples, such restrictions may be based forexample on inferred location-based semantics (e.g. using a camera orvision sensors for detecting the door width). The system composesvarious post configurations based on their sizes to determine theoptimal join topology which may be based on mapping a semantic network(e.g. endpoint) model to areas to be covered by particular posts.

While the previous example may incorporate wheeled smart posts,alternatively, or in addition, it may incorporate drone type semanticposts comprising a copter module for lifting; it is to be understoodthat the smart post modules including the copter module may comprisemotors/engines, propellers, servomotors, electronic speed controller,analog blocks, digital blocks and actuators.

In a wheeled-copter based application the system activates the wheeledmodule and/or copter module of the smart posts based on routing andsemantic inference on the semantic model. The semantic network model maybe mapped to land-based locations and/or aerial based locations.

The system may create a composite formation of posts/units (e.g. FIGS.13 and 14 ) in order to improve sensing and/or capabilities. In anexample, the system infers low count, low trust rating, unreliableand/or conflicting semantics by posts at a location. Further, the systemmay infer that the coverage of location and/or a mapped semantic networkmodel in the field of sensing is not adequate. Thus, the system composesthe smart posts to improve coverage and/or reliability of semanticinference. In further examples, the system combines smart posts in aformation based on their capabilities; in addition, it may use a goal ormission-based inference to form the composite based formation.

The antenna elements module 7 (see also FIG. 3 ) may comprise panels ofmulti-array antenna elements 22; the panels may be disposed on theexterior of the trunk in a specific pattern (e.g. hexagonal). While insome embodiments the panels are fixed, in other embodiments the panelsare automatically movable and composable and can be moved and organizedin various patterns on the exterior of the trunk (e.g. two panels on twosides of the hexagon combine in a larger panel that can be oriented aswell in various directions). The antenna elements and panels mayincorporate RF and optical frontends, transmit/receive modules, ADC,DAC, power amplifiers, DSPs, semantic units and other analog and/ordigital blocks and components. Other post modules might incorporatesimilar elements in some embodiments.

The vision, or optical, module 8 may incorporate arrays of camera and/orvision sensors 23 disposed in a circular pattern about the perimeter ofan optical module such as in the example illustrated in FIG. 2B, or maybe arranged within an upper dome in an array pattern, or may incorporatedome cameras or others, such as illustrated in FIG. 2A (showing theouter dome, with the optical elements or cameras not visible within thedome). The cameras and/or vision sensors may be of time of flight typecomprising laser and/or photonic elements for emitting and receiving(e.g. laser diodes, photodiodes, avalanche photodiodes-linear/analogmode, Geiger-mode, etc., edge-emitting lasers, vertical cavity surfaceemitting lasers, LED, fiber laser, phototransistors).

The control module 5 is used to process the information of the roboticunit and for communication via the sensing and wireless modules (e.g.antenna modules). The posts may communicate with each other (such asdepicted in FIG. 10B, showing three separate smart posts labeled posts1, 2, and 3) or with the distributed computing infrastructure (asillustrated in FIG. 10A, also showing three posts, numbered 1, 2, and 3)using any wireless protocols. Alternatively, or in addition, the postsmay communicate through wiring and/or cabling embedded in the connectingbands and/or clips while the latching and clipping mechanisms comprisecabling connectors (e.g. specialized connectors, RJ45, Ethernet, serialinterface etc.). It is understood that the control module functionalitymay be distributed amongst other modules, posts, computers and computerbanks.

As mentioned, the clipping and fixation mechanisms allow the posts toreconfigure in various setups, topologies, zones and settings. Therobotic distributed infrastructure allows such reconfigurations based onsemantic inference including localization, hierarchical network modelsand zoning. While various clipping and attaching modules and mechanismshave been presented and depicted it is to be understood that suchclipping and attaching mechanism may be standardized in someapplications.

The following example presents the embodiment of a port of entryoperation using a combination of smart posts and real time semantictechnologies.

Semantic IOT composable cloud and real time semantic technologiesprovide adaptive real time and just in time operational intelligence andcontrol while aggregating disparate sources of information.

They function based on semantic engines which interpret semantic modelsand semantic rules and thus are highly adaptable to the operational orsimulated context. They are highly suitable for integrating multi-domainknowledge including capabilities, interdependencies, interactions,actions and what-ifs scenarios. Real-time semantic technologiesunderstand the meaning of data from various sources and take appropriateactions; they provide real time situational awareness and automation. Asemantic engine performs semantic knowledge discovery by using a set ofadaptive artifacts including a semantic model which may be defined by auser, ingested or learned by the system. The semantic model comprisesthe representation and mapping of informational flows and groupings tomeanings (e.g. linguistic based terms related to objects, states,control actuation, groups, relationships, routes etc.); the semanticsystem guides the inference in the semantic model based on semanticrules and routes which specify how the system should behave. Thecapacity of a semantic system inference capabilities increases as thesemantic model evolves through modeling and learning. The semantic modelis defined as linguistic based operational rules and routes. Further,the semantic model may be associated with hierarchical semantic networkmodels for further management of paths, fluxes/flows, routes andsemantic inference. In a semantic network model, the semantics areassigned to artifacts in an oriented graph and the system adjusts thesemantic network model based on ingested data and semantic inference.The semantic network graph comprises endpoints and oriented links in apotential hierarchical structure with graph components representinganother semantic network graph. As data is ingested from the smart postsfunctional modules, the semantic engine is able to perform inferences inreal time, providing semantic intelligence, adjusting the semantic modeland potentially executing actions. Semantics and/or semantic attributesare language or symbol terms and structures that have a meaning. Themeaning in particular contexts and circumstances is established bysemantic models including semantic groups and semantic routes; whenassociated with a semantic network model they may be associated withartifacts in a semantic graph representation of the system.

A semantic group represents a grouping of artifacts based on at leastone semantic relationship.

Semantic routes comprise a collection of semantic artifacts (e.g.semantics, semantic groups, semantic routes, semantic network modelartifacts etc.) and potential synchronization times; the semantic routesmay be represented as a semantic and/or as a semantic group of semanticartifacts. They may be also associated with semantic rules (e.g. timemanagement, access control, factoring, weighting, rating etc.).

Semantic routes may be represented, associated and/or identified withsemantic artifacts (e.g. semantic and/or semantic group) and as suchthey benefit from general semantic modeling and analysis.

Semantic routes may be organized in a hierarchical manner with semanticroutes comprising other semantic routes. Such hierarchical structure maybe recursive.

The semantic routes may be grouped in semantic groups and participate insemantic inference.

Semantic routes associated with a semantic network model may be used forartifact (e.g. traveler, smart post) routing within modeledenvironments.

In this disclosure we will refer as semantic rules to all rules thatallow semantic inference comprising composition and management plansincluding time management, access control, weighting, ratings, rewardsand other factors (e.g. risk).

Semantic routes may be used as and/or to implement operational rules andguidelines. For example, the system is provided with allowable, desired,non-allowable and/or non-desired routes. In an example a route specifiesthat HOT CROWDED SPACES ARE NOT PLEASANT and also that CLOSE TO SHOPPINGIS NICE and thus semantic post units and/or groups provisioned with suchroutes when inferring a HOT CROWDED SPACE semantic (e.g. via semanticcomposition) for an area would select the previous rules and determine afurther route comprising COOLING and/or DIVIDE crowds to areasencompassing (or closest) to SHOPPING locations. It is to be understoodthat in this example areas may be mapped to endpoints in a network modelrepresentation of a physical space and the system would execute thecommands in the routes based on the existing or deployable capabilitiesat mapped endpoints (e.g. areas). In an example, the DIVIDE semantic maybe achieved via further semantic inference comprising smart postrouting/guidance topologies, semantic shaping, semantic orientationand/or semantic augmentation. Further, the COOLING semantic may beachieved if the areas comprise cooling capabilities and/or semantics(e.g. via a fixed air conditioning fan module which may be potentiallyattached to a smart post unit). Some semantic inference techniques areexplained in a family of patent applications such as US20140375431, thecontent of which is incorporated by reference. In further examples, ifthe system infers that an area and/or endpoint is associated withsemantic artifacts (e.g. HEAT related, etc.) which have high(entanglement) entropy, drifts, shifts and/or factors as related withCOOLING then the system may pursue the COOLING leadership and/orcapabilities. It is to be understood that the inference at an endpointmay be based on semantic profiles of the (semantic) identities at thearea/endpoint and thus the high shift and/or entropy semantics may bebased and/or related with at least one (semantic) identity and/or(composite) profile. If the area and/or endpoint semantics are inferredbased on multiple identities (during at least on a projected hysteresis,diffusion and/or semantic time interval) then the system may pursueCOOLING capabilities (e.g. until the entropy, drift and/or factorsadjust to sensible (composite profiling) (hysteresis) levels, healthrisk of HEAT decreases etc.).

In further examples, the system determines goals and further optimizedsemantic shapes of groups of posts (or cars) to be realized withinparticular semantic budgets (e.g. based on energy consumption/quanta,fuel related quanta, entropy etc.). Such shapes and/or zones may bebased on semantic groups and/or presence at particular areas and/orendpoints. In further examples such shapes may be associated with areas,endpoints, trajectories and/or sub-models. It is to be understood thatthe shaping may take in consideration the fitting of the posts within anarea or endpoint based on semantic inference on dimensions, mappings,semantics and/or further semantic analysis; further, the shaping may bebased on semantic orientation and drift analysis between the goal groupshape and the current group shape. Further, the system may usedissatisfaction, concern and/or stress factors in order to assess thefitting of posts within various areas.

In some examples, semantic shaping is used to optimize traffic flowswhere the system determines the best shapes, zones and endpoints forgroups of vehicles at particular times or particular areas.

In other examples, semantic shaping and semantic analysis may be used tooptimize container and/or artifact storage in particular areas and/orvolumes (e.g. mapped to semantic models).

Semantic inference uses semantic analysis comprising semanticcomposition, semantic fusion, semantic routing, semantic resonance,semantic indexing, semantic grouping, semantic time and/or otherlanguage based semantic techniques including semantic shift, entailment,synonymy, antonymy, hypernymy, hyponymy, meronymy, homonymy.

In an example, a semantic group containing all the synonyms for “great”is stored and used in semantic inference. In some cases, the groupcomprises semantic factors assigned to semantic components to expressthe similarity within a group or with the semantic attributes definingthe group. In further examples, the system stores a semantic group forthe same semantic (e.g. (“running”, “runnin”); (“o'leary”, “oleary”, “oleary”) etc.). In another example, the system stores separate identitiesand/or groups for “cat” and/or “c.a.t.” as they are associated withdifferent semantics; further, during semantic inference the systeminfers leadership to “c.a.t.” over “cat” or vice-versa based on exactsemantic identification (e.g. match the exact semantic form and/oridentity) and/or semantic view. In the examples, the system may haveinferred from ingested data that artifacts (e.g. “cat” and “c.a.t.”)have and/or are associated with different semantics (e.g. semanticidentities) and thus the system is able to identify and/or create suchsemantic identities and/or semantic groups. Analogously, the system mayinfer that the ingested artifacts are associated with the same semantic(e.g. (“running”, “runnin'” and thus the system may create a semanticidentity and/or group to reflect the association and for furtheroptimization.

It is to be understood that the leadership may be determined by couplingof semantic analysis and/or circumstances (e.g. location/localization,language, semantic profiles, roaming etc.).

The semantic analysis comprises semantic techniques such as synonymy,semantic reduction, semantic expansion, antonymy, polysemy and others.In an example, the user specifies lists of synonyms, antonyms and otherlists that are semantically related. The elements in a list are bythemselves related through semantic groups via semantic attributes orsemantics (e.g. SYNONIM, ANTONIM).

Real time semantic technologies optimize processes and resources byconsidering the meaning of data at every level of semantic AI inference.Real time semantic technologies are well suited for providingsituational awareness in ports of entries while further providing aframework for adaptive integration.

Semantic IOT infrastructure based on smart posts/robots and real timesemantic technologies can provide precise counting, times and routing atthe port of entries.

The ports of entry layout may be modeled through hierarchical semanticnetwork models wherein the endpoints are associated with smart postsensing and locations in the layout; further, oriented links betweenendpoints represent the flows, transitions and the semantics of trafficat the modeled/instrumented points. The area, location and sensing basedsemantic network model is recursive and thus can be used to achieve thedesired level of granularity in the mapped environments.

Semantics may be associated with sensing/data flows, checkpointattributes, traveler attributes and further, the semantic modelcomprises semantic routes and how semantics compose. Flows/fluxessemantics and interdependencies may be modeled and learned via semanticmodeling and inference.

The counting of people in monitored queues, areas or endpoints may bebased on the traveler-based semantics inferred based on transitioning oflinks in the semantic layout/sensing model. Further, the system guidesthe semantic inference for traveler waiting times using semantic timeand semantic intervals. The semantic time and semantic intervals allowtime inference based on semantics. Further, a semantic time is indexedbased on the context of operation. Thus, semantic time and semanticintervals ensure that the time inference takes places in the mostaccurate context of operation. By using semantic intervals and adaptivesemantics for inference a semantic system achieves predictive semantics.

In an example, a checkpoint for foreign nationals is timed based on thetransitions in the semantic network model. In simplest terms, forexample, at one checkpoint gate it may take a foreign national fromcountry A (Fa) 1 min to be cleared by an officer and a foreign nationalfrom country B (Fb) 2 min. Thus, every time when the systems infers,potentially based on semantic interval contexts (e.g. arrival of aflight and arrival at the checkpoint), that there are foreign nationalsfrom country B at the checkpoint, it may index the waiting timeaccordingly. While the previous time indexing has been based on a singleattribute (citizenship), other attributes or categories can be used forindexing the time (e.g. age of travelers, traveler status, visa type,system speed, network speed etc.). This kind of operational inferenceand analytics is hence very accurate and performed in real time withoutthe need of storing large amounts of data or continuously utilizinglarge compute resources. Further, patterns in time and space are learnedby semantic IOT through semantic intervals.

A semantic system also groups artifacts based on semantic inference anduse those groups in further semantic inference. In our example thesystem may detect object types or complex semantics based on suchsemantic groups (e.g. group sensors, settings and detections and infermeanings, infer travelers by detecting flows of grouping of detections,features, clothing items and belongings; infer that a person is carryinga red bag etc.). It is to be understood that the Semantic IOT is adistributed composable cloud and as such it distributes, groups, composeand fusion various modalities detections in an optimized manner; asmentioned, the modalities may comprise a diverse spectrum ofelectromagnetic sensing.

In our example, the counting may be based on the transitions in thesemantic network model; thus, when a link in the semantic network modelis transitioned as detected by the smart posts and their modalities, thesystem infers a particular semantic (e.g. TRAVELER ENTER CHECKPOINT 1 orTRAVELER EXITS CHECKPOINT 1). Semantic composition and fusion of suchsemantics allow the coupling of detected semantics in and with time(e.g. counting the number of semantics/travelers at checkpoints,estimating waiting times or other general or personalized semantics) inthe most flexible, efficient and optimized manner and utilizing aminimum amount of resources thus decreasing system costs. Other systemsmay not employ such flexibility, optimization, fusion and modelingtechniques and hence they are not able to provide the same capabilities,coherence, accuracy and cost effectiveness.

The system will use adjustable inferable model semantics for mapping thetype of service (e.g. CITIZENS AND PERMANENT RESIDENTS mapped totransition links from the checkpoint inbound to checkpoint outbound),for counting (e.g. derive the number of people based on the transitionsin the semantic network model), for speed of processing (traveler ratein an interval of time), to derive general or personalized sentimentinferences (e.g. VERY FAST, FAST, SLOW), for traveler semantic routing,experience rating, personalization and so forth.

Semantic automation and augmentation ensure actions in various domains;in an example, the coupling of the command and control model to semanticautomation and augmentation may implement automatic or semi-automaticguiding, routing and access control in port of entry environments.

Based on the level of the autonomy employed through semantic automationand semantic augmentation the technology may be used to automate varioustasks and provide semantic intelligence in various forms includingdisplay, sound, actuation, electric, electromagnetic, etc.

Solutions for port of entries (e.g. airports) includes developingsemantic network models to be deployed on the distributed semantic cloudand mapped to a semantic sensing infrastructure. The semantic sensinginfrastructure may include smart semantic posts/appliances comprisingsensors, batteries and semantic sensing units which can be deployedthroughout the port of entry.

The assumption in this example is that there are no available sensors atthe monitored locations and as such the system uses semantic sensing forfeeding the semantic network model. Semantic systems provide semanticfusion and as such, the system may integrate various data sources and/oradditional sensing infrastructure for contextual accuracy and moreprecise inference. One example is when the smart posts comprise one ormore of radiofrequency, camera/optical/infrared sensors. It is to beunderstood that camera/optical/infrared sensors can be selected fromcost effective solutions such as low-cost ones designed for mobiledevices. The radiofrequency devices/sensors may function in microwavefrequencies range (e.g. 2.4 Ghz to 80 Ghz) or higher.

It is preferred that such sensors be easily deployable andreconfigurable in various environments and as such they may be one ormore of the following: mobile post deployed sensors and fixed postsdeployed sensors. While the smart semantic posts/appliances may bemobile in some environments, they can deploy as fixed on walls or otherstructures.

The smart posts may comprise Li-Ion batteries which may provide extendedfunctioning time for the attached sensors and semantic units. Thebattery posts provide real time awareness of their charging status whichallow easy maintenance whether manual or automatic for charging and/orbattery replacement. Alternatively, they may be plugged in at any timeat a permanent or temporary supply and/or charging line. For easiermaintenance of the battery powered devices, they may be deployed in amutual charging and/or external charging topology comprising RF and/orrobotic charging components.

The microwave devices/sensors may comprise multiple sensing elements(e.g. 4 to 256) which allow the sensors to detect steer and optimize thebeam, frequency, detection and communication patterns. More antennas maybe present thus providing more scene interpretation capabilities anddata that can be fused for knowledge discovery (e.g. adapting andchanging radiation patterns, adapting frequencies and polarizations).

In the simplest case, post sensors are disposed to capture transitionpatterns in at least one semantic network model which may be stored ateach post comprising control module logic. Thus, with each transition inthe model, the system detects and counts semantics of objects dependingon the determined semantic of travel (e.g. PERSON IN CHECKPOINT GATE 2,PERSON OUT CHECKPOINT etc.). These deployments are straightforward incontrol areas and boarding sterile corridors where the flow is guidedthrough lanes and corridors thus allowing for less shadowing andmultipath effects. Thus, the counting in these areas can be very preciseby instrumenting the lanes and/or corridors with smart posts or othersensing artifacts. For example, in a checkpoint lane the system uses oneor two posts for lane ingestion and one or two posts for departuredetection.

In such lanes and corridors, the location based semantic network modelscomprise fewer artifacts than in non-lane-controlled areas, thusminimizing the processing and optimizing power consumption. Also, therelevant detection happens in near field for both optical and microwaveand as such the data interpretation would be straightforward. Further,semantic system's capability of changing and adapting the sensingpatterns allows the reduction in the number of collection points and thenumber of sensors and thus maximum flexibility in deployments.

In non-lane-controlled areas and corridors the system may employ a morecomplex near to far field semantic model of locations which are mappedto semantic sensing detection techniques. The semantic engine fuses theinformation in the semantic network model.

In an example, the system uses radio frequency polarization diversity toimprove detection in multipath environments. The smart semantic sensorsmay employ diversity antennas and/or use coupling of antenna elements toadjust electromagnetic radiation, polarizations, optimize frequenciesand so forth.

Further, based on inferred topologies the system may reposition thesmart posts in the environment and coordinate them to clip to each otherin order to delimitate and realize the semantic zones and topologiesrequired for traffic flow control.

In FIGS. 8A and 8B, posts are disposed in a guiding lane configuration.In FIG. 8A, a first series of posts labeled a-f are on a left side of anentry point 40 and a second series of posts g-n are on a right side ofthe entry point. The entry point may be a location of passport control,boarding a craft, check-in, or any other point at which persons areprocessed or allowed to pass. Initially, the posts are arranged closelyadjacent one another, and preferably with their associated ropes orbelts attaching adjacent posts to one another but with the belts eitherretracted within the respective post or hanging in a slack fashion. InFIG. 8B, some of the posts have moved and been extended to increase thelength of the traffic lane between the posts. Specifically, posts d, e,and f have moved, as has post n, as indicated by the arrows and thevisibility of the belts that have been extended. In FIG. 8C, the postshave extended to the fullest extent, forming the longest line possiblefor the assembled collection of posts.

At the setup of FIG. 8A, one or more of the sensors (cameras, antennas,analog and/or digital blocks/devices etc.) of one or more of the postsscans the region between the posts, indicated as region 41. Upon thedetection of persons standing in the region, the system determines thatan extension is required. The particular logic may vary and bedetermined as above, but for example may require a plurality of postsa-f and/or g-n to detect static persons in the area, waiting but notmoving quickly.

In FIG. 8B, one or more of the posts continues to scan the area,including region 42 occupying the terminal end of the lane 50 defined bythe opposite pairs of posts. Most preferably, at least the end posts fand n provide input indicating the presence of persons standing in thatregion. In other versions, all of the posts, or at least a largersubset, also provide such an input which is used by the controller todetermine whether to extend the posts yet again and thereby form alarger line. Finally, as shown in FIG. 8C, the posts have exhaustedtheir reach. Most preferably, the controller is programmed with a map ofthe area surrounding the entry point, and also tracks the location ofeach of the posts, in order to direct the individual posts whether tomove in a direction linearly away from a prior post (for example, withreference to FIG. 8C, in a direction from post I to post k), or to moveat an angle with respect to at least a pair of prior posts (for example,in a direction from post k to post l, or from m to n).

In FIG. 9 we show a perimeter delimitation configuration. The perimeterin the illustrated example is defined by posts a-d, though a differentnumber of posts may be used. The posts combine to define a perimeter 51having an internal area 52. In an example, the system infers and/or auser specifies an area and/or a semantic associated with it. The areamay be delimited based on anchor points and/or the edges.

In FIG. 10 we show various deployment options in which the postscommunicate wirelessly and/or process information in a distributed cloudinfrastructure. While in embodiment A they may use an externaldistributed cloud infrastructure, in embodiment B they use their owninternal processing capabilities in a distributed cloud mesh topology;it is to be understood that the system may use any capabilities, whetherinternal and/or external to infer and configure compo sable cloudtopologies. Also, their movement, positioning and coupling may be basedon semantic network models whether at sensor, post, semantic group,infrastructure or any other level. It is to be understood that thegrouping of smart posts in various topology, processing and cloudconfigurations may be based on semantic grouping based on semanticinference on inputs, outputs, sensing etc.

Any one or more of the posts may travel independently about a region,such as generally indicated with reference to posts 1, 2, and 3 shown inin FIGS. 10A and 10B, without being tethered to one another. In such aconfiguration, the posts collect the optical, audio, or otherinformation from sensors, cameras, antennas, analog and/or digitalblocks and/or devices, front-ends etc., which may then be passed alongdirectly to other posts as indicated in FIG. 10B, and/or to a central ordistributed control infrastructure 100 as shown in FIG. 10A. The controlinfrastructure 100 may be a central computer communicatively coupledwith the plurality of distributed devices. It should be appreciated thatany of the features described in this disclosure as being performed by“the system” may be performed by the control infrastructure in acentralized fashion, or may alternatively be performed in a distributedfashion by a distributed system including a plurality of controlstructures and/or computer components on the posts or robotic devices.

In other embodiments the posts may comprise master-slave configurations.In such configurations the master posts controls at least one slavepost. The slave posts may comprise less functionality and/or be lesscapable than the master post (e.g. lacking full suite of sensors and/oractuators, smaller batteries, lacking displays etc.). The master postmay control the movement and/or deployment of slave posts. In someexamples the master post detects and control the positioning of slaveposts. For example, an airport may use units of groupings of master andslave posts (e.g. groupings of at least one master and at least fiveslaves). Such units may be deployed and yield composable topologies andformations.

In further examples, the robotic posts formations and/or componentsthereof may be based on semantic groups which may comprise leadershipsemantic artifacts.

Master-slave configurations may be represented as semantic groups withthe master units attaining leadership in particular configurationsand/or environments.

The smart posts may comprise billboards, displays, actuators, speakersand other forms of semantic augmentation allowing them to conveyinformation.

In a further example of utilization, the smart posts may be deployed inkey areas and provide guidance via semantic augmentation. The semanticaugmentation may comprise advertising. In some embodiments the smartposts and/or groups may be designed as for general use, however, whenthey receive a mission and a target they may adapt to the mission andtarget. In the airport example a unit of posts may receive the missionto provide guidance and/or lane formation to a particular airline. Thus,the posts may deploy to the targeted airline airport area and providethe semantic augmentation related to the airline; such information maycomprise airline name, flight information, airline specific advertisingand so on. The specific information may be received and/or downloadedfrom a specialized advertising service and/or cloud (e.g. airlinecloud). The deployment of the post to the airline area may be based onthe previous knowledge on the location of the airline, sensing andguidance.

In other examples the posts may deploy in areas that are inferred as ofhigh risk and/or congested. Thus, once the distributed cloud infers suchconditions it automatically initiates the deployment of units and/ortopology reconfiguration; the initialization of operations may takeplace based on semantics inferred at any inference capable post. Forexample, in the high-risk areas the posts may be deployed for achievinga topology that reduces the overall risk (e.g. guiding the travelersthrough lower risk areas and/or routes, dividing the crowds based onboarding zones, traveler/visa status, risk etc.).

In some embodiments the posts are deployed in location and/or areas forwhich the system infers particular semantics. For example, for alocation the system may infer a semantic of HAZARDOUS or SHOPPING TOOCROWDED and thus the system may dispose posts and/or units to containthose zones and/or guide travelers to other routes that do not containsuch areas. Thus, posts deployed for such purpose may indicate viasemantic augmentation (e.g. display and/or audio, wireless beaconing)the zone semantics and directions to follow by travelers in proximity;it is to be understood that proximal semantic augmentation may betriggered when travelers are detected in proximity. The travelers mayinclude people, vehicles and any other moving artifacts considered bythe system.

While we refer to inference, it is to be understood that it may be basedon inference at a single post/unit, a group of posts/units, distributedcloud and any combination of the former. The semantic system functionsas a distributed architecture in various configurations comprising butnot limited to semantic group computing, edge computing, cloudcomputing, master-master, master-slave etc.

In some embodiments, the system issues missions and/or commands to poststhat are in particular locations, areas and/or endpoints and haveinferred specific semantics. For example, the system issues commands tothe posts that have been deployed to HAZARDOUS semantic areas and haveassociated semantics of MASTER POST, BATTERY HIGH and/or STAND POST UNITDISPLAY TIME 1 HOUR. For example, such commands may be used to displayflight information, routing information (e.g. for guiding out ofhazardous area), advertisements and any other type of augmentativeinformation. In the previous example the selection of posts may beassociated with a semantic group defined by composite semanticsdetermined by a semantic route (e.g. STAND POST UNIT DISPLAY TIME). Itis to be understood that the system may select and/or command a semanticgroup of posts based on compositional semantics (e.g. STAND POST UNIT)and other sematic group hierarchies formed based on semanticcomposition.

It is to be understood that the previous exemplified semantics, semanticgroups and/or semantic routes may be evaluated and/or inferred by thesystem on a linguistic relationship basis including semantic shift,entailment, synonymy, antonymy, hypernymy, hyponymy, meronymy, holonomy,polysemy. Thus, in an example, a HAZARDOUS semantic inference may bebased and/or reinforced (e.g. higher weights) using synonyms and/orrelated semantic groups (e.g. UNSAFE). In other examples, the HAZARDOUSsemantic may be coupled and/or reinforced (e.g. lower weights) usingantonyms and/or related semantic groups (e.g. SAFE).

Real time semantic technologies and semantic analysis allow for adaptiveintelligent systems that can be used for multi domain intelligence,automation and autonomy.

Those technologies are based on semantic analysis techniques of whichsome are explained in patent Pub No 20140375430.

Semantic analysis comprises semantic composition, semantic fusion,semantic routing, semantic orientation, semantic gating, semanticinference and/or other language based semantic techniques includingsemantic shift, entailment, synonymy, antonymy, hypernymy, hyponymy,meronymy, holonomy.

In this disclosure we will refer as semantic rules to all rules thatallow semantic inference comprising composition and management plansincluding time management, access control, weighting, ratings, rewardsand other factors. Semantic artifacts include semantics, semanticgroups, rules, semantic routes, semantic views, semantic view frames,semantic models and any other artifact used in semantic analysis.

Semantic technologies allow the interpretation of inputs and datastreams into operational semantic knowledge which may compriseintelligent related outputs, user interfaces, control and automation.The inputs, data streams and operational semantic knowledge may berelated to sensing, signals, images, frames, multimedia, text,documents, files, databases, email, messages, postings, web sites, mediasites, social sites, news sites, live feeds, emergency services, webservices, mobile services, renderings, user interface artifacts andother electronic data storage and/or providers. Further, ingestedartifacts and/or semantic groups thereof may be linked and/or associatedwith semantic model artifacts. In some examples,paragraphs/sections/headers from email, markup formatteddata/objects/files, chat or posting messages and/or web pages may berepresented. Further, semantic identification of such paragraphs (e.g.attributing a news article to its author, newspaper, group etc.) mayallow semantic profiling and factorization at any level of semanticidentification. Thus, the semantic artifacts associated with thesemantic identification and semantic profiles may be further factorizedbased on the semantic analysis of encountered tags, markups and/or theirvalues (e.g. certain artifacts are associated and/or factorized based onan underlined and/or particular font, header etc. as detected based ontags and/or markups); further, such inferred factorized semanticartifacts may be used to modify and/or mask the associated tags and/ormarkup values in documents. In some examples, the summary content insome documents is masked, not showed and/or not rendered in preview modein particular circumstances (e.g., when user not present or not lookingat semantic device).

An integral part of the semantic knowledge discovery is a semantic modelwhich represents a set of rules, patterns and templates used by asemantic system for semantic inference.

The capacity of a semantic system's inference capabilities may increaseas the semantic model evolves through semantic inference, modeling andlearning.

A semantic field represents the potential of semantic knowledgediscovery for a semantic system through information processing andinference.

A system achieves a particular semantic coverage which represents theactual system capabilities for semantic knowledge generation. Hence, thesemantic coverage can be expanded by adding new streams or inferenceartifacts to the operational semantic capabilities of the system.

In some examples the semantic coverage is related to the semanticnetwork model coverage capabilities (e.g. the area covered, theresolution covered at the lowest or highest endpoint hierarchy, thenumber of hierarchical levels etc.). Further, the semantic coverage maybe related to sensing and inference modalities available for givensemantic network model artifacts (e.g. a semantic coverage is extendedif a system comprises two sensing modalities as comparable to only onemodality of similar capabilities).

The semantics may be assigned to artifacts in the semantic network model(graph) including endpoints and links. Dependencies between semanticsand/or artifacts may be captured and/or determined by oriented linksbetween the endpoints, hierarchy and/or path composition. As such, agroup dependent semantic group may be represented as an orientedgraph/subgraph with the causality relationships specified as orientedlinks (e.g. from cause/causator to effect/affected and/or vice-versa).Additionally, the elements in the model may be hierarchical andassociated with any semantic artifacts.

The system may comprise symptoms—cause—effect semantic artifacts (e.g.semantic routes). In an example the system determines symptoms such asP0016 ENGINE TIMING WHEN COLD and 80% DIRTY OIL and as such infers apotential cause of 80% TIMING SOLENOID ISSUE and further projected risk(e.g. IMMEDIATE, WHEN VERY COLD etc.) of ENGINE BREAKDOWN.

Semantic collaboration means that disparate systems can work together inachieving larger operational capabilities while enhancing the semanticcoverage of one's system semantic field.

A semantic flux is defined as a channel of semantic knowledge exchange,propagation and/or diffusion between at least a source and at least adestination. By using semantic information from semantic fluxes, areceiving system improves semantic coverage and inference.

A semantic flux connection architecture may be point to point, point tomultipoint, or any combination of the former between a source anddestination. Semantic fluxes may be modeled as a semantic network modelwhether hierarchical or not.

Semantic fluxes can be dynamic in the sense that they may interconnectbased on semantic inference, semantic groups and other factors. In anexample, a semantic flux A is connected with a semantic flux B at firstand later it switches to a point to point configuration with semanticflux C.

A composite semantic flux comprises one or more semantic groups ofsemantic fluxes, potentially in a hierarchical and/or compositionalmanner; further all the information from the composite flux isdistributed based on the composite flux interconnection, semanticrouting and analysis.

Dynamic flux configurations may be based on semantic groups andhierarchies. For example, flux A and B are semantically grouped at firstand flux A and C are semantically grouped later. In further examplessemantic groups interconnect with other semantic groups and/or fluxes,potentially in hierarchical and compositional manner.

Semantic fluxes may transfer information between semantic engines and/orsemantic units comprising or embedded in access points, gateways,firewalls, private cloud, public cloud, sensors, control units, hardwarecomponents, wearable components and any combination of those. Thesemantic engine may run on any of those components in a centralizedmanner, distributed manner or any combination of those. The semanticengine may be modeled in specific ways for each semantic unit withspecific semantic artifacts (e.g. semantics, semantic groups etc.) beingenabled, disabled, marked, factorized, rewarded and/or rated in aspecific way.

Semantic fluxes may use any interconnect technologies comprisingprotocols, on-chip/board and off-chip/board interconnects (e.g. SPI,I2C, I/O circuits, buses, analog and/or digital blocks and components,diodes, varactors, transistors etc.), CAN, wireless interfaces, opticalinterfaces and fibers and so on. Additionally, or alternatively,semantic fluxes connect via semantic sensing units comprising semanticcontrolled components, including those previously enumerated and othersenumerated within this application.

Semantic fluxes and/or streams may also connect other objects orartifacts such as semantic display units, display controls, userinterface controls (e.g. forms, labels, windows, text controls, imagefields), media players and so on; semantic fluxes may be associatedand/or linked to/with display controls in some examples. Such objectsmay benefit from the semantic infrastructure by publishing, gating,connecting, routing, distributing and analyzing information in asemantic manner. Such objects may use I/O sensing, authentication andrendering units, processes, components and artifacts for furthersemantic analysis, gating, routing and security. In an example, thesemantic gating routes the information based on authentication andsemantic profiles. In further examples, display control or userinterface components and/or groups thereof aredisplayed/rendered/labeled, enabled, access controlled or gated based onsemantic analysis, semantic profiles, semantic flux and gatingpublishing. As such, the system identifies the context of operation(e.g. comprising the user, factors, indicators, profiles and so on) anddisplays coherent artifacts based on coherent inference.

Various types of controls and/or dashboards can be displayed based onsemantic routes and/or semantic profiles (e.g. groups specific, semanticidentity specific, user specific etc.).

In further examples, the system flows the information between semanticfluxes and gates based on semantic routing and semantic profiles.

In some examples, the system monitors the change of data (e.g. viaanalyzing a rendering, bitmap, user interface control/artifact, window,memory buffer analysis, programming interface, semantic inference etc.)in the user interface and perform semantic analysis based on the newdata and the mapping of the changed data.

In further examples, the system infers and identifies display semanticsartifacts (e.g. of an airport app window, messaging app, geographicinformation system window, input/output control etc.), activations,locations and a further semantics based on I/O data (e.g. touch/mouseclick) on the window and the system maps and creates semantic artifacts(e.g. models, trails, routes etc.) from such inference. It is to beunderstood that the mapping may be hierarchical, relative to theactivated artifacts in a composable manner. Alternatively, or inaddition the mapping may be absolute to the display surface whethercomposed or not (e.g. comprising multiple display artifacts and/orsub-models).

For semantic systems the “time” may be represented sometimes as asemantic time or interval where the time boundaries, limits and/orthresholds include semantic artifacts; additionally, the time boundariesmay include a time quanta and/or value; sometime the value specifies theunits of time quanta and the time quanta or measure is derived fromother semantic; the value and/or time quanta may be potentiallydetermined through semantic indexing factors.

The semantic indexing factors may be time (including semantic time),space (including location semantics) and/or drift (including semanticdistance/drift) wherein such indexing factors may be derived from oneanother (e.g. a semantic of VERY CLOSE BY might infer a semantic ofSUDDEN or SHORT TIME with potentially corresponding factors). As such, asemantic system is able to model the space-time-semantic continuumthrough semantic inference and semantic analysis.

In further examples, the semantic indexing may be used to index riskfactors, cost factors, budgets and so on.

Semantic indexing represents changes in the semantic continuum based onsemantics and/or semantic factors with some examples being presentedthroughout the application.

In an example, the system determines a first semantic at a firstendpoint/link and a second semantic for an endpoint/link; further, thesystem determines a location for a new endpoint on an oriented linkand/or endpoint determined by the first and/or second endpoint/linkbased on an indexing factor associated with a composite semantic whichis a combination of the first semantic and the second semantic. Inanother example, the composite semantic is a combination between asemantic associated with a source model artifact (e.g. endpoint or link)and a destination model artifact and the indexing factor associates anew model artifact on the path/link between the source model artifactand the destination model artifact. The indexing factor may beassociated with a semantic factor calculated/composed/associated with asemantic artifact; an indexing factor may be used to index semanticfactors. Once the system infers an indexing factor for a semantic it mayupdate the semantic model and add endpoints on all semantic endpointsand/or links associated with the semantic via semantic relations orsemantic groups. Further the system may redistribute the existing ornewly inferred semantics on the new determined endpoints and establishnew oriented links and rules.

In an example the system determines an object/feature boundary based onindexing wherein the system indexes and/or merges/splits the on and/oroff boundary artifacts until it achieves a goal of inferringhigh-quality object semantics.

The system may map hierarchical semantic models to artifacts in thesemantic field and infer semantics at various hierarchical levels,wherein higher hierarchical levels provide a higher semantic level ofunderstanding of feature and identification semantics (e.g. nails, legs,hands, human, man, woman, John Doe, classmates etc.).

During inference the system maps semantic network models to objectsartifacts and so on and performs further inference in the semanticfield. In some examples the mapping is based on boundary conditions anddetection.

In other examples the indexing is used in what-if and projectedanalysis, mapping and/or rendering the semantic model based on goals andforward/backward hierarchical semantic inference. In such examples thesystem may invalidate and/or delete related artifacts post indexation(e.g. first and/or second endpoints/links).

The indexing factors may be related with indexing values related withactuation and or commands (e.g. electric voltages, currents, chemicaland biological sensors/transducers etc.).

The indexing factors may have positive or negative values.

Semantic factors and indexing factors may be used to activate andcontrol analog or digital interfaces and entities based on proportionalcommand and signal values. The system may use indexed and/or factorizedanalog and digital signals to control such electronic blocks,interfaces, other entities, electric voltages, currents, chemical andbiological sensors and transducers etc.

The system may use variable coherent inferences based on at least one(variable) coherence/incoherence indicators and/or factors. In someexamples, the semantic analysis of circumstances associated with thecoherence/incoherence factors deem the variable coherent inference ascoherent and/or incoherent based on the (semantic) factorization of thecoherence/incoherence indicators and/or factors.

The semantic composition infers, determines and guides the context ofoperation. Semantic analysis may determine semantic superposition inwhich a semantic view frame and/or view comprises multiple meanings(potentially contradictory, high spread, high entanglement entropy,incoherent, non-composable—due to lack of composability, budgets and/orblock/not allowable rules, routes and/or levels) of the context. Theinference in semantic views may yield incoherent inferences whichdetermine incoherent superposition artifacts (e.g. semantic factors,groups, routes etc.). Alternatively, or in addition, the inference insemantic views yield coherent inferences which determine coherentsuperposition artifacts (e.g. semantic factors, groups, routes etc.).The semantic expiration may control the level of superposition (e.g. thefactor of conflictual meanings or a sentiment thereof). Thesuperposition is developed through semantic analysis including semanticfusion in which a combined artifact represents the composition and/orsuperposition of two or more semantic artifacts. Thus, semanticexpiration may be inferred based on semantic fusion and superposition.In an example, the system performs fusion (e.g. potentially via multipleroutes) and infers that some previous inferred semantics are not neededand therefore learns a newly inferred semantic time management rulewhich expires, invalidates and/or delete them and the semantic model isupdated to reflect the learned rules and artifacts. Analogously, thesystem may use projections to associate and/or group ingested and/orinferred signals and/or artifacts with projected semantic artifacts; itis to be understood that such learned semantic groups, rules and further(associated) semantic artifacts may expire once the system performfurther analysis (e.g. collapses them, deems them as nonsensical, decaysthem etc.).

The system learns artifacts via multiple semantic routes. Further, thesemantic routes are factorized by the multiplicity of associatedsemantic artifacts. In an example the system factorizes a semantic routebased on an association with an inferred semantic; further, the inferredsemantic is factorized based on the associated semantic routes.

Coherent semantic groups may be inferred based on coherent and/or safeinferences (with less need of evaluating blocking routes and/or rules onleadership and/or group semantics) comprising the members of the group.

The coherency and/or entanglement of semantic groups may increase withthe increased semantic gate publishing, factorizations, budgets and/orchallenges within the group. Further, increases in coherency and/orentanglement may be based on high factorized collaborative inferencesincluding inference and/or learning of sensitive artifacts (e.g. basedon a sensitivity and/or privacy factor, risk of publishing (to othergroups), bad publicity, gating, weights and/or access control rules).

Factors and/or indicators (e.g. likeability, preference, trust, risketc.) may influence the coherency and/or entanglement of semanticgroups.

The increased affirmative coherency and/or resonance of (affirmative)semantic groups may increase likeability/preference/satisfaction/trustfactors and/or further affirmative factors. Analogously, the decreasedaffirmative coherency and/or resonance of semantic groups may decreaselikeability/preference/satisfaction/trust factors and/or furtheraffirmative factors.

The system may prefer non-affirmative coherency and/or resonance of(non-affirmative) semantic groups in order to increase the semanticspread.

The affirmative factors may comprise affirmative-positive and/oraffirmative-negative factors.

Affirmative-positive factors are associated with confidence, optimistic,enthusiastic indicators and/or behaviors. Analogously,affirmative-negative factors are associated with non-confidence,pessimistic, doubtful, unenthusiastic indicators and/or behaviors.

Affirmative-positive and/or affirmative-negative may be used to modelpositive and/or negative sentiments. Further, they may be used to asses,index and/or project (realizations) of goals, budget, risks and/orfurther indicators.

Coherent and/or resonant semantic groups exhibit lower entanglemententropy on leadership and/or group semantics while incoherent semanticgroups may exhibit higher entanglement entropy. Semantic indexing may beused to implement hysteresis and/or diffusion. Semantic indexing may beinferred based on diffusion (e.g. atomic, electronic, chemical,molecular, photon, plasma, surface etc.) and/or hysteresis analysis.Further, the system may use semantic diffusion to implement semantichysteresis and vice-versa. Semantic superposition may be computed onquantum computers based on the superposition of the quantum states.Alternatively, other computing platforms as explained in thisapplication are used for semantic superposition.

The system may budget and project superposition factors. In someexamples, a user may specify the maximum level and/or threshold intervalof superposition for inferences, views, routes, goals and otherinference and viewing based artifacts; further, it may specifysuperposition budgets, factors and goals.

The semantic field comprises a number of semantic scenes. The system mayprocess the semantic field based on semantic scenes and eventually thefactors/weights associated to each semantic scene; the semantic scenesmay be used to understand the current environment and future semanticscene and semantic field developments. A semantic scene can berepresented as a semantic artifact. In some examples the semantic scenescomprise localized semantic groups of semantic artifacts; thus, thesemantic scenes may be represented as localized (e.g. simple localizedand/or composite localized) semantic models and groups.

A semantic group represents a grouping of artifacts based on at leastone semantic relationship. A semantic group may have associated and berepresented at one or more times through one or more leaders ofartifacts from the group. A leader may be selected based on semanticanalysis and thus might change based on context. Thus, when referring toa semantic group it should be understood that it may refer to its leaderor leaders as well. In some examples, the leaders are selected based onsemantic factors and indicators.

A semantic group may have associated particular semantic factors (e.g.in semantic views, trails, routes etc.).

A semantic view frame is a grouping of current, projected and/orspeculative inferred semantics. In an example a semantic field viewframe comprises the current inferred semantics in the semantic field; asemantic scene view frame may be kept for a scene and the semantic fieldview frame is updated based on a semantic scene view frame. A peripheralsemantic scene may be assigned lower semantic factors/weights; as suchthere may be less inference time assigned to it. Additionally, thesemantic group of sensors may be less focused on a low weight semanticscene. In an example, a semantic scene comprising a person riding abicycle may become peripheral once the bicycle passed the road in frontof the car just because the autonomous semantic system focuses on themain road. A semantic view frame may be represented as a semantic groupand the system continuously adjusts the semantic factors of semantics,groups, objects and scenes.

Semantic view frames may be mapped or comprised in semantic memoryincluding caches and hierarchical models.

For a peripheral semantic scene, the semantic system retains thesemantics associated with that scene (e.g. semantic scene view frame)longer since the status of the scene is not refreshed often, or theresolution is limited. In some examples the refreshment of the scenes isbased on semantic analysis (e.g. including time management) and/orsemantic waves and signals. A predictive approach may be used for thesemantic scene with the semantic system using certain semantic routesfor semantic inference; semantic routes may be selected based on thesemantics associated with the semantic scene and semantics associatedwith at least one semantic route. In the case that the peripheral scenedoesn't comply with projections, inferred predicted semantics orsemantic routes the semantic system may change the weight or thesemantic factor of that semantic scene and process it accordingly.

In an example, once the bicycle and the rider becomes peripheral thesystem may refocus the processing from that scene; if there is somethingunexpected with that semantic scene (group) (e.g. a loud sound comesfrom that scene, in which case the system may infer a “LOUD SOUND”semantic based on the sound sensors) the system may refocus processingto that scene.

In further examples, the system blocks/gates some sounds and/orfactorizes others based on the perceived peripherality and/or importance(e.g. based on location, zone, semantic identity, semantic etc.).Further, the system may infer leadership semantic artifacts associatedwith the non-peripheral and/or peripheral scenes and use them to enhancethe non-peripheral scenes and/or gate peripheral scenes.

Analogously with peripheral scene analysis the system may implementprocedural tasks (e.g. moving, climbing stairs, riding a bicycle etc.)which employ a high level of certainty (e.g. low risk factor, highconfidence factor etc.). Thus, the procedural semantic analysis andsemantic view frames may comprise only the procedural goal at hand (e.g.RIDING THE BICYCLE, FOLLOW THE ROAD etc.) and may stay peripheral ifthere are no associated uncertainties (e.g. increasing risk factor,decreasing confidence/weight factor etc.) involved in which casesemantic artifacts may be gated to/from higher semantic levels.

The system uses semantic analysis, factors and time management todetermine the reassessment of the scenes/frames and/or the semanticgating for each scene/frame (and/or semantic groups thereof).

In rapport with a semantic view, the semantic view frames which areperipheral, predictive and/or have highly factorized cues (e.g. based onlow entanglement entropy) the semantic time quanta and/or budgets mayappear to decay slower as they may require less semantic time and/orentanglement entropy budgets.

Semantic inference based on semantic composition and/or fusion allow forgeneralization and abstraction. Generalization is associated withcomposing semantic/s and/or concepts and applying/assigning them acrossartifacts and themes in various domains. Since the semantics areorganized in a composite way, the system may use the compositionalladder and semantic routing to infer semantic multi domain artifacts.

Generalization rules may be learned for example during semantic analysisand collapsing artifacts composed from multiple semantic fluxes and/orgated semantics.

In some examples generalization rules learning comprises the inferenceand association of higher concepts and/or semantic artifacts (e.g.rules, routes, model artifacts etc.) in rapport with fluxes, signals,waveforms and/or semantic waves.

It is to be understood that particular semantics may be available,associated and/or inferred only within particular hierarchical levels,endpoints, semantic groups (e.g. of endpoints, components etc.) and/orstages. Thus, when a semantic signal and/or wave transitions in thesemantic network, those semantics may be decoded and/or inferred only inthose particular contexts.

A semantic group may comprise artifacts which change position from oneanother. The semantic engine identifies the shapes and/or trajectoriesof one artifact in relation with another and infers semantics based onrelative shape movement and/or on semantic shape. The trajectory andshapes may be split and/or calculated in further semantic shapes, routesand/or links where the system composes the semantics in shapes or linksto achieve goals or factors. The semantic engine may determine semanticdrift and/or distance between artifacts based on endpoints, links,semantics assigned to artifacts (including semantic factors), indexingfactors and/or further semantic analysis.

The system may infer sentiments for the distance and motion semanticsbased on the context. In an example, if the system is in a 75% TAKEOVERFRONT CAR drive semantic as a result of a 75% SLOWER FRONT CAR and it isin a semantic route of FRONT CAR FAR, INCOMING CAR FAR it may infer aREASONABLE RISK for takeover while further using a semantic trail ofFURTHER APPROACH THE FRONT CAR, PRESERVE VISIBILITY; as hence, the riskis reassessed based on the semantic trail, view inferences and furthersemantic routes (e.g. CLOSED GAP, FRONT CAR 90% SLOW, INCOMING CAR 40%FAST, CAN ACCELERATE FAST 70% and thus the risk indicator for TAKEOVERFRONT CAR is still within contextual preferences and/or biases) and thedrive semantic affects the semantic routing and orientation (e.g.takeover actions). It is to be understood that the system may adjust thefactor for the drive semantics (e.g. 25% TAKEOVER FRONT CAR) based onfurther inferences and risk assessment (e.g. 40% SLOWER FRONT CAR, 90HIGH TRAFFIC→NOT WORTH RISK) and/or delay and/or expire the drivesemantic altogether; it is understood that the delay and/or expirationmay be based on semantic indexing (e.g. time, space) and/or timemanagement wherein the system uses existing and/or learned artifacts. Infurther examples, the system infers a CAR CRASH associated with asemantic group identity in a semantic view and as hence it adjusts theroutes, rules and/or model to reflect the risk factors associated withthe particular semantic group (e.g. in the semantic view context). It isto be understood that the system may use semantic (view) shaping toinfer and/or retain particular semantic artifacts reflecting contextscaptured in (hierarchical) semantic views potentially in a hierarchicalmanner. The semantic system also groups artifacts based on semanticinference and use those groups in further semantic inference. In ourexample the system may detect object types or complex semantics based onsuch semantic groups (e.g. group sensors, settings and detections andinfer meanings, infer travelers by detecting flows of grouping ofdetections, features, clothing items and belongings; infer that a personis carrying a red bag etc.).

It is to be understood that the semantic system is a hybrid composabledistributed cloud and as such it distributes, groups, compose and fusionvarious modalities detections in an optimized manner. The modalities maycomprise a diverse spectrum of electromagnetic sensing.

A semantic stream is related with a stream of non-semantical andsemantic information. A semantic stream may transmit/receive data thatis non-semantical in nature coupled with semantics. As an example, if acamera or vision system mounted on a first location or first artifactprovides video or optical data streaming for the first artifact, thefirst artifact may interpret the data based on its own semantic modeland then transfer the semantic annotated data stream to another entitythat may use the semantic annotated data stream for its own semanticinference based on semantic analysis. As such, if a semantic scene in avideo stream, frame or image is semantically annotated by the firstsystem and then transferred to the second system the second system mayinterpret the scene on its own way and fusion or compose its inferredsemantics with the first system provided semantics. Alternatively, oradditionally, the annotation semantics can be used to trigger specificsemantic drives and/or routes for inference on the second semanticsystem. Therefore, in some instances, the semantic inference on thesecond semantic system may be biased based on the first system semanticinterpretation.

In some examples a semantic stream may be comprised from semantic fluxchannel and stream channel; such separation may be used to savebandwidth or for data security/privacy. As such, the semantic flux isused as a control channel while the stream channel is modulated,encoded, controlled and/or routed based on the semantics in the semanticflux channel. While the channels may be corrupted during transmission,the semantic flux channel may be used to validate the integrity of boththe stream channel and semantic flux channel based on semantic analysison the received data and potentially correct, reconstruct or interpretthe data without a need for retransmission.

It is to be understood that the semantic stream may comprise semanticwave and/or wavelet compressed and/or encrypted artifacts.

In another example, the semantic flux channel distributes information topeers and the stream channel is used on demand only based on theinformation and semantic inference from flux.

Further, the system may use authorization to retrieve data from the fluxand/or stream channel; in an example, the authorization is based on anidentification data/block, chain block and/or the authorization ispursued in a semantic group distributed ledger.

The system may associate semantic groups to entities of distributedledgers. The distributed ledger semantic group may be associated withmultiple entities and/or users; alternatively, or in addition, it may beassociated with identities of an entity, for example, wherein thedistributed ledger comprises various user devices. Sometime thedistributed ledger is in a blockchain type network.

Virtual reconstruction of remote environments, remote operation anddiagnosis are possible based on semantic models and real time semantictechnologies. The objects from the scenes, their semantic attributes andinter-relationships are established by the semantic model andpotentially kept up to date. While such reconstruction may be based ontransfer models, in addition or alternatively, they may be based onvirtual models (e.g. based on reconstruction of or using semanticorientation and shaping).

Sometimes, the ingesting system assigns a semantic factor (e.g. weight)to the ingested information; the assigned factor may be assigned tofluxes/streams and/or semantics in a flux/stream.

Themes are semantic artifacts (e.g. semantic, semantic group) that areassociated with higher level concepts, categories and/or subjects.

The semantic routes may be classified as hard semantic routes and softsemantic routes.

The hard-semantic routes are the semantic routes that do not change. Attimes (e.g. startup or on request), the system may need to ensure theauthenticity of the hard-semantic routes in order to ensure the safetyof the system. Thus, the hard semantic routes may be authenticated viacertificates, keys, vaults, challenge response and so on; thesemechanisms may be applicable to areas of memory that store the hardsemantic routes and/or to a protocol that ensure the authentication ofthose routes. In some examples the hard semantic routes are stored inread only memories, flashes and so on. Semantic routes may be used forpredictive and adaptive analysis; in general, the semantic routescomprise a collection of semantic artifacts and potentialsynchronization times; the semantic routes may be represented as asemantic group of semantic artifacts including semantics, groups, rulesetc.; they may be identified based on at least one semantic. They may bealso associated with semantic rules (e.g. time management, accesscontrol, factoring, weighting, rating etc.).

While the semantic routes are used for semantic validation and/orinference they may be triggered and/or preferred over other semanticroutes based on context (e.g. semantic view, semantic view frame).

Semantic routes may be represented, associated and/or identified withsemantic artifacts (e.g. semantic and/or semantic group) and as suchthey benefit from general semantic modeling and analysis. Semanticroutes may comprise or be associated with semantic artifacts, semanticbudgets, rewards, ratings, costs, risks or any other semantic factor.

In some instances, semantic routes representation comprises semanticgroups and/or semantic rules.

Semantic routes may be organized in a hierarchical manner with semanticroutes comprising other semantic routes. Such hierarchical structure maybe recursive.

The semantic rules may be grouped in semantic groups and participate insemantic inference.

Analogously with the hard-semantic routes the semantic rules may beclassified as hard or soft.

The semantic routes and rules may encompass ethics principles. Ethicsprinciples of semantic profiles and/or semantic groups may model“positive” behavior (e.g. DO, FOLLOW artifacts etc.) and/or “negative”behavior (DON'T DO, DON'T FOLLOW artifacts etc.) and their associatedfactors; as specified the “positive” and “negative” behavior may berelative to semantic profiles and/or semantic groups.

Ethics principles may be based and/or relative to semantic profilescomprising ethics semantic routes and rules; in some examples, theethics principles are comprised in hard semantic and/or highlyfactorized trails, routes and/or rules. Semantic analysis may use ethicsprinciples for semantic factorization. In some examples, duringinference, positive behavior artifacts within or as related withsemantic profiles and/or semantic groups and associated circumstanceswould be preferred to negative behavior based on a reward to risk ratiointerval thresholding. The reward may be based on publicity (e.g.gating) of behavior based inference; further the risk may entail badpublicity (e.g. gating of semantics which would cause “negative”behavior inference (relative to the particular semantic identities,semantic profiles) in collaborative semantic fluxes and/or semanticgroups.

Projections of publicity (e.g. positive or negative) may be inferredthrough propagation and/or diffusion of gated semantics through variousleadership artifacts and/or semantic fluxes. Thus, because particularfluxes may act as leaders, it is important to project the propagationand/or diffusion based on goals. In some examples, in cases where thebudgets are low, the system may diffuse semantics which will first reacha “positive influence” leader as opposed to a “negative influence”leader. In further examples, the system may perform semanticorientation, routing and/or gating in order to achieve the publicityand/or influencing goals. It is to be understood that a “positiveinfluencer” leader is relative to the goals of publisher and notnecessarily towards the goal of the influencer (e.g. the influencer mayhave a negative behavior towards (NURSE) (JANE) artifacts but becausethe influencer's negative factors/ratings on (NURSE) (JANE) artifactspropagate and/or diffuse in groups which have low ratings, high riskand/or are “negatively” factorized of routes comprising the influencerthen the overall goal of generating positive ratings on those groups maybe achieved.

The representation of semantic groups may include semantic factorsassigned to each group member. In some examples semantic factorsdetermine the leaders in a group in particular contexts generated bysemantic analysis. Sometimes, membership expiration times may beassigned to members of the group so, when the membership expires themembers inactivated and/or eliminated from the group. Expiration may belinked to semantic rules including time management rules; further factorplans with semantic factors and semantic decaying may determineinvalidation or inactivation of particular members. The semantic routesmay be organized as a semantic model and/or as a hierarchical structurein the same way as the semantics and semantic groups are organized andfollowing similar semantic inference rules.

The system may infer semantics by performing semantic inference on thesemantic groups. In an example, the system may compose and fuse twosemantic groups and assign to the new group the composite semanticsassociated with the composition of the first group semantics and thesecond groups semantics. Group leader semantics may be composed as wellbesides the member semantics. In some cases, only the leader semanticsare composed. By combining the leader semantics with member semantics,semantic timing and decaying the system may infer new semantic rules(e.g. semantic time rules).

Further, in an example, the system performs semantic augmentation whileinferring and/or identifying a person (JOHN) performing an activity(BASEBALL); using semantic analysis based on multiple semantic trailsand routes it infers that JOHN's skills factors are high and pursues agoal to EXPRESS OPINION TO BILL of the inference based on a semanticroute of IMPRESSED SO EXPRESS OPINION TO PAL. Thus, based on a route foran template of PRONOUN VERB ADJECTIVE and further, based on grouping ofJOHN as a (THIRD, ((3 RD), 3^(rd))) PERSON based on PRONOUN routing, theinference may establish that a leadership semantic is 3 RD PERSON; assuch, when being routed within the semantic network it may selectartifacts that comply with such leadership semantic in semantic groupsand further routes. Further, the system may have semantic groups such asPRONOUN ((1 ST PERSON, ALL GENDERS, “I”), (2 ND PERSON, ALL GENDERS,“YOU”), (3 RD PERSON, MALE, “HE”), (3 RD PERSON, FEMALE, “SHE”)); andfurther IS (3 RD PERSON, ALL GENDERS); and further GOOD (ALL PEOPLE (1ST PERSON, 2 ND PERSON, 3 RD PERSON), ALL GENDERS (MALE, FEMALE)) andthus the system may determine a semantic augmentation of JOHN IS GOODbased on a leadership semantic of 3 RD PERSON and other semanticanalysis as appropriate.

In a further example of abstraction learning, the system may infer fromBILL's voice signals that JOHN IS GOOD and because has semantic groupsthat associate IS with VERB and GOOD with ADJECTIVE it may infer asemantic route, template and/or semantic group of PRONOUN VERBADJECTIVE; and further, similar and/or other semantic artifacts and/orrelationships whether factorized or not. Further factorization may occuron such learned artifacts based on further semantic analysis.

Semantic decaying occurs when a quantifiable parameter/factor associatedwith a semantic artifact decays or varies in time, most of the timetending to a reference value (e.g. null value or 0); as such, if theparameter is negative decaying is associated with increases in thesemantic factor value and if the factor is positive decaying isassociated with decreases in factor's value. Sometimes, when thesemantic decays completely (e.g. associate factor is at the referencevalue or interval) the semantic may be inactivated, invalidated ordisposed and not considered for being assigned to an artifact, semanticroute, semantic rule, semantic model and/or inference; further, based onthe same principles the semantic is used in semantic group inference andmembership. The system asks for feedback on group leadership, semanticfactors and/or group membership. The feedback may be for example fromusers, collaborators, devices, semantic gates and other sources.

In some examples, the reference decaying value is associated withapplied, activation/deactivation, produced or other voltages andcurrents of analog or digital components and/or blocks. In furtherexamples such values are associated with chemical or biologicalcomponents and mixing elements.

Quantifiable parameters such as semantic factors may be assigned orassociated with semantics. The semantic factors may be related toindicators such as weights, ratings, costs, rewards, time quanta orother indicators and factors. In some cases, the semantic factors areused to proportionate control parameters, hardware, I/O, analog anddigital interfaces, control blocks, voltages, currents, chemical andbiological agents and/or any other components and/or interfaces. Thosequantifiable parameters may be adjusted through semantic inference.

The semantic factors may be associated to a semantic (e.g. semanticidentity) implicitly (directly) or explicitly via a semantic indicatorin which a semantic specifies the type of indicator (e.g. risk, rating,cost, duration etc.) and the semantic factors are associated with thesemantic via semantic indicators.

The semantic factors may be associated to a semantic via semantic groupswhich may comprise the semantic, the semantic indicators and/or thesemantic factors in any combinative representation of a semantic group.As such, the semantic factors participate in semantic inference andanalysis.

When a semantic factor is assigned directly to a semantic the system mayassociate and interpret the indicator associated with the factorimplicitly based on context. Alternatively, or in addition, the factoris assigned to various indicators based on context.

The factors are associated with degrees, percentages of significance ofsemantic artifacts in contextual semantic analysis.

Implicit or explicit semantic indicators may be defined, determinedand/or inferred based on a context. In an example an indicator isinferred based on goals. In other examples multiple indicators aredetermined for a particular goal inference. In some cases, the systemmay substitute an indicator over the other, may infer or invalidateindicators based on semantic inference. As with other semantic rules thesystem may comprise indicator rules that specify the interdependenciesbetween semantic indicators based on time management, semantic time,weights, ratings, semantics, semantic groups, semantic routes, semanticshapes and other semantic artifacts.

Semantic indicator rules and any other semantic rules may be associatedwith semantic artifacts, semantic factors and indicators. As such thesystem may perform recursive inference which is controlled by factorrules, decaying and other semantic techniques. Further, the semanticrules are inferred, invalidated, learned and prioritized based on suchfactor techniques; in general, the semantic techniques which apply tosemantic artifacts apply to semantic rules.

Semantic factors may be associated with symbols, waveforms and patterns(e.g. pulsed, clocked, analog etc.). The association may be directthrough semantics or semantic model. Further the semantic factors may beused in hierarchical threshold calculations (HTC) algorithms todetermine a mapping to an endpoint. Decaying and semantic factors may beinferred and learned with semantic analysis. In some examples the systemlearns decaying and factor semantic rules and semantic routes.

The semantic learning may include inferring, linking and/or grouping amultitude of trails and routes based on variation of circumstances (e.g.location, anchor, orientation, profile, environment, sensor, modality,semantic flux, route etc.).

In further examples, the system optimizes the inference by factorizingand/or learning relationships in the network semantic model. In someexamples the system uses the semantic analysis (e.g. based onaction/reaction, action/reward etc.) to reinforce routes and paths (e.g.based on rewards, goals etc.). As such, when the system infers artifactsthat are not against the DO NOT guidelines (e.g. blocked semantics,rules, routes), it may collapse the semantic artifacts, link and/orfactorize them. In further examples, the system may cache such routesand/or map them at lower or higher level depending on factorizationand/or theme. Further, when the system infers semantic artifacts whichare against DO NOT (BLOCK) rules and/or guidelines it may associateand/or collapse them with semantic artifacts based on DO semantics,artifacts and/or rules. It is to be understood that the DO and DO NOTsemantic artifacts may be associated with time management rules (e.g. itmay be allowed to DO a BATTERY DISPOSAL in a HAZARDOUS RECYCLINGcircumstance while in all other circumstances the DO NOT artifactsapply).

When the system infers a gating rule it may adjusts and/or invalidaterules, routes and/or further artifacts which may activate gating basedon such rule. If the gating is a block/deny rule the system may decaysuch artifacts. If the gating is based and/or controlled on intervalfactor thresholding the system may adjust the semantic rules.

A semantic time budget may comprise a time interval or time quantarequired to perform an inference; in some examples the semantic timebudget is based on semantic time. Semantic cost budgets comprise anallowed cost factor for the semantic inference. Semantic budgets maycomprise and/or be associated with other factors and indicators (e.g.risk, reward etc.). Semantic budgets may be based onpredictions/projections based on a variety of factors and may beassociated with semantic composition, time management rules, accesscontrol rules and/or semantic routes. Also, they may be correlated withthe hardware and software components characteristics, deployment andstatus in order to generate a more accurate budget inference.

Semantic budgets may include inferences about the factors to be incurreduntil a semantic goal or projection is achieved; also, this may compriseassessing the semantic expiration, semantic budget lapse and/or semanticfactor decaying. Such assessment of factors may be interdependent insome examples.

Sometimes, the semantic thresholds and/or decaying are based on a biaswhere the bias is associated with particular semantics, factors and/orbudgets.

In an example, semantic budgets may be specified by semantic timeintervals. Further, semantic budgets may be specified based on decaying,factor and indexing rules.

In further examples the semantic budgets may comprise and/or beassociated with prices (e.g. utilizing 10 quanta budgets in a computingand/or energy grid environment comprises 0.4 W power consumption and/or0.05$ charge etc.). It is to be understood that the inferences may bebased on any budget including time, price, risk, reward and/or otherfactors and indicators. Further, the system may comprise time managementrules specifying that the utilization of 10 quanta budgets in particularcircumstances (e.g. time management) may entail additional bonus budgetsmade available (potentially also having an expiration time management)to the user and/or flux and thus the system may associate and/or indexbudgets with particular components, units, fluxes, routes and furtherfactorize them (e.g. factorize a PREFERRED indicator for the bonusprovider flux in rapport with particular inferences).

Goal based inferences allow the system to determine semantic routes,trails and/or budgets.

Semantic routes are used for guiding the inference in a particular way.In an example, a user specifies its own beliefs via language/symbologyand the system represents those in the semantic model (e.g. usingsemantic routes, semantic groups etc.).

The semantic inference based on semantic routes may be predictableand/or speculative in nature. The predictability may occur when thesemantic routes follow closely the semantic trails (portions of thehistory of semantics inferred by the system). Alternatively, the systemmay choose to be more pioneering to inferences as they occur and followsemantic trails less closely. In an example, a car may follow apredictive semantic route when inferring “ENGINE FAILURE” while mayfollow a more adaptive semantic route when inferring “ROLLING DANGER”.The predictability and/or adaptivity may be influenced by particularsemantic budgets and/or factors.

Such budgets and/or factors may determine time management and/orindexing rules. In some examples, the system infers/learns a semantictime rule and/or indexing factor based on low inferred predictabilityfactor wherein the inference on a semantic artifact is delayed until thepredictability increases.

Further, the system identifies threats comprising high risk artifacts inrapport to a goal. The system may increase speculation and/orsuperposition in order to perform inference on goals such as reducingthreats, inconsistencies, confusion and/or their risk thereof; in casethat the goals are not achieved (e.g. factors not in range) and/orconfusion is increasing the system may increase dissatisfaction, concernand/or stress factors. The system may factorize dissatisfaction, stressand/or concern factors based on the rewards factors associated with thegoal and the threat/inconsistency risk factors. It is to be understoodthat such factors and/or rules may be particular to semantic profilesand/or semantic views. In some examples the threats and/orinconsistencies are inferred based on (risk) semantic factors (e.g. riskof being rejected, risk of not finding an article (at a location) etc.).

When the system follows more predictable routes and the projections donot match evidential inference the system may infer and/or factorizedissatisfaction, concern and/or stress factors based on semantic shiftsand/or drifts.

Dissatisfaction, concern and/or stress factors may be used to infersemantic biases and/or semantic spread (indexing) factors and, further,the system may infer semantic (modality) augmentation in order to reducesuch dissatisfaction, concern and/or stress factors. It is to beunderstood that the augmentation may be provided and/or be related withany device based on circumstantial inference and/or semantic profiles.In an example, a detected sound (e.g. from a sound modality) is tooloud, repetitive and/or unusual pitch which indexes the concern and/orstress factors and further determines the adjustment,composition/smoothing and/or cancelation of the sound; further, tactile(modalities) actuators may be inferred to be used to alter and/or divertthe inference on the sound receptor trails to tactile trails and tofurther increase the semantic spread and thus potentially reducing theconcern and/or stress factors. It is to be understood that the systemmay monitor the dissatisfaction, concern and/or stress factorscorrelated with the augmentation artifacts applied to reduce them andfurther perform semantic learning based on correlation.

The system may infer, adjust and/or factorize likeability, preference,satisfaction, trust, leisure and/or affirmative factors based on high(entanglement) entropy inference in rapport with (higher)dissatisfaction, concern and/or stress artifacts and vice-versa.

Confusion may decrease as more semantic routes/trails and/or rules areavailable and/or are used by the system.

Confusion thresholds may shape semantic learning. Thus, lower confusionthresholds may determine higher factorizations for a smaller number ofroutes/trails and/or rules associated to (past and/or future)(projected) inferences. Higher confusion thresholds may determine lowerfactorizations for a larger number of routes/trails and/or rulesassociated to (past and/or future) (projected) inferences.

As the system comprises more semantic routes/trails and/or rules withsimilar factorizations (e.g. no strong leadership artifacts) thesuperposition may increase as the evidence inference comprises moresemantic spread.

For lower confusion thresholds the assessment of evidence (e.g. truthartifacts (provided) in the semantic field and/or flux) may be moredifficult as the existing highly factorized artifacts are fewer and theymay shape fewer highly factorized inferences with less semantic spreadand decreased superposition.

Dissatisfaction, concern and/or stress factors may increase if higherfactorized semantic artifacts in the inferred (projected) circumstancesdo not match evidence and/or evidence inference leads to confusion.

Dissatisfaction, concern and/or stress factors may be used to indexand/or alter factorizations of the semantic artifacts used in evidenceinference, in order to decrease such factors in future inferences, basedon evidence inference and/or challenges (e.g. flux, user etc.).

The system may infer goals such as maintaining and/or gaining leadershipwhich might signify involvement and/or importance in (group) decisionmaking and further factorizations of dissatisfaction, concern and/orstress factors.

Increase in dissatisfaction, concern and/or stress factors may signifythat the (group) pursued goals where not optimal. Further, suchinferences may determine adjustments of routes, rules and/or furtherartifacts including factorizations of leadership, groups and/or semanticfluxes.

Predictability and/or speculative factors inferences may be associatedwith factors related to dissatisfaction, concern and/or stress factors(e.g. they may alter semantic spread). Further, authoritative rules mayaffect such factors as they may determine high consequential risk and/orfear factors.

The semantic route may be represented as a semantic artifact (e.g.semantic, semantic group) and participate in semantic analysis andsemantic modeling.

Semantic route collapse occurs when during an inference the semanticengine determines (through generalization and/or composition forexample) that a semantic route can be represented in a particular orgeneral context through a far more limited number of semantics that theroute contains. With the collapse, the system may create a new semanticroute, it may update the initial semantic route, it may associate asingle semantic associated with the original semantic route. In certainconditions the system may inactivate and/or dispose of the collapsedsemantic route if the system infers that are no further use of thesemantic route (e.g. through semantic time management and/orexpiration). The semantics that may result from a route collapse may becompositional in nature. Additionally, the semantic engine may updatethe semantic rules including the semantic factors and as such it loosens(e.g. decaying) up some relationships and strengthen (e.g. factorizing)others.

The system creates and/or updates semantic groups based on semanticroute collapse. Further, the system may collapse the semantic modelartifacts (e.g. endpoints and/or links associated with the semanticroute to a lesser number and/or to higher level artifacts).

Semantic route collapse may determine semantic wave collapse (e.g. lowmodulated semantic wave) and vice-versa.

Semantic wave collapse may depend on the frequency of electromagneticradiation received by semantic systems, components, endpoints and/orobjects. In an example, composition and collapse doesn't happen unlessthe electromagnetic radiation frequency reaches a threshold whichfurther allows (the semantic unit, object's semantic wave) thegating/outputting of semantics. In some examples the threshold frequencyis associated with the minimum electromagnetic frequency generatingphotoelectrons emissions (e.g. by photoelectric effect). It isunderstood that by tuning the composite, absorptive, dispersive,diffusive and/or semantic artifacts of (nano) meshes the thresholdfrequency at a location may be tuned and thus allowing fasthyperspectral semantic sensing.

The system builds up the semantic routes while learning eitherimplicitly or explicitly from an external system (e.g. a user, asemantic flux/stream). The build-up may comprise inferring anddetermining semantic factors. The semantic routes may be used by thesemantic system to estimate semantic budgets and/or semantic factors.The estimate may be also based on semantics and be associated withweights, ratings, rewards and other semantic factors.

The semantics that are part of the semantic route may have semanticfactors associated with it; sometimes the semantic factors areestablished when the semantic route is retrieved in a semantic viewframe; as such, the factors are adjusted based on the context (e.g.semantic view frame factor). While the system follows one or moresemantic routes it computes semantic factors for the drive and/orinferred semantics. If the factors are not meeting a certain criterion(e.g. threshold/interval) then the system may infer new semantics,adjusts the semantic route, semantic factors, semantic rules and anyother semantic artifacts.

Sometimes the system brings the semantic route in a semantic view frameand uses semantic inference to compare the semantic field view and thesemantic view frame. The system may use semantic route view frames toperform what if inferences, pioneer, speculate, project and optimizeinferences in the semantic view. At any given time, a plurality ofroutes can be used to perform semantic inference and the system maycompose inferences of the plurality of routes, based on semanticanalysis, factors, budgets and so on. The analysis may comprise semanticfusion from several semantic route view frames. Sometimes the semanticroute does not resemble the expected, goal or trail semantics and assuch the system updates the semantic routes and trails, potentiallycollapsing them, and/or associate them with new inferred semantics;additionally, the system may update the semantic factors, updatesemantic groups of applicable semantic routes and any other combinationsof these factors and/or other semantic techniques.

The system learning takes in consideration the factorization of semanticrules and/or routes; thus, the learned semantic artifacts may beassociated with such rules and factors (e.g. “DRIVE IN A TREE” has ahigh risk and/or fear factor etc.). In some cases such semanticartifacts are compared and/or associated with the hard semantic routesand/or artifacts; the inferred semantic artifacts may be discardedinstead of learned if they make little sense (e.g. prove to beincoherent and/or highly factorized in relation with particular—stable,factorized, high factorized—semantic trails/routes, semantic drift toohigh etc.).

In further examples, the system receives and/or infers a compositesemantic comprising a potential semantic goal and an associatedentangled (consequence) semantics (e.g. having high/lowundesirability/desirability factors) for pursuing/not-pursuing and/ormeeting/non-meeting the goal (e.g. JUMP THE FENCE OR GO BUST, JUMP THEFENCE AND GO TO EDEN, JUMP THE FENCE AND GO TO EDEN OR GO BUST);further, the entangled semantic artifact may determine adjustment of thegoals factors (e.g. risk, weight, desirability etc.) and furtherprojections. It is to be observed that in the example the entanglemententropy is high due to consequences having a high relative semanticentropy (in rapport with the goal and/or in rapport to each other, theyare being quite different even opposite or antonyms). In furtherexamples, the entangled consequence can be similar and/or identical withthe goal (e.g. GO BUST OR GO BUST) and as such the entanglement entropyis low. It is to be understood that the entanglement entropy may beassociated with the semantic factors inference (e.g. when theentanglement entropy is high the factors and/or indexing may be higher).

In the previous example, it is to be understood that EDEN may activatedifferent leaderships based on semantic analysis and/or semanticprofiles. For example, the previous inferences and/or profiles may havebeen related solely with EDEN a town in New York state and hence thesemantic route associated with EDEN, TOWN, New York may have a highersemantic leadership than EDEN, GARDEN, GODS. However, for particularsemantic profiles the EDEN, GODS may bear a higher semantic leadershipthan EDEN, TOWN. As mentioned before where there is a confusion factorthe confused system may challenge the user and/or other fluxes (e.g.such those initiating/challenging the goal of JUMP THE FENCE and/orconsequences) for additional information (e.g. which EDEN?).

When the confusion is high the system may decay and/or invalidate thesemantic artifacts (e.g. routes, rules etc.) which generated confusion.When the confusion is low the system may factorize such artifacts.

The leadership semantics may be based on inferences and/or semanticsassociated with endpoints, links, locations, semantic groups and/orfurther semantic artifacts associated with the subject (e.g. challenger,challenged, collaborator, user, operator, driver etc.).

Semantic drift shift and/or orientation may be assessed based onsemantic entropy and/or entanglement entropy. Analogously, semanticentropy and/or entanglement entropy may be based on semantic drift,shift and/or orientation.

During a semantic collapse the system may assess whether the collapsiblesemantic is disposable possible based on semantic factors and decaying;if it is, the system just disposes of it. In the case of semantic wavecollapse it may reject, filter or gate noisy and/or unmodulated wavesignal.

Sometimes the disposal is deferred based on semantic time management.

The system continuously adjusts the semantic factors and based on thefactors adjusts the routes, the semantic rules, semantic view frames andso on. If the factors decay (e.g. completely or through a threshold,interval and/or reference value) the system may inactivate, invalidateand/or dispose of those artifacts.

In further examples, new semantic artifacts may be associated withhighly factorized routes based on the activity associated with the routeand thus the new semantic artifact may be also highly factorized and/orretained longer (e.g. in semantic memory). Analogously, a highlyfactorized semantic artifact when associated with a semantic routedetermines the higher factorization and/or longer retainment of thesemantic group.

Semantics are linguistic terms and expression descriptive and indicativeof meanings of activities on subjects, artifacts, group relationships,inputs, outputs and sensing. The representation of the semantics in thecomputer system is based on the language of meaning representation (e.g.English) which can be traced to semantics, semantic relationships, andsemantic rules. Sometimes, when the system understands more than alanguage and symbology, the relationship between the languages isrepresented through semantic artifacts wherein the second languagecomponents are linked (e.g. via a first language component into asemantic group) with the first language; sometimes, the system choses tohave duplicated artifacts for each language for optimization (e.g. bothlanguages are used often and the semantic factors for both languages arehigh) and model artifacts are linked and/or duplicated.

In an example, the system has a semantic group of associated to CARcomprising GERMAN AUTO, SPANISH COCHE, FRENCH VOITURE. When performingtranslation from the language of the meaning representation to GERMANthe system uses the GERMAN as a leadership semantic and thus the systemperforms German language narrative while inferencing mostly in thelanguage of meaning representation (e.g. English). However, the systemmay optimize the GERMAN narrative and inference by having, learning andreorganizing the particular language (e.g. GERMAN) semantic waves,semantic artifacts, models and/or rules as well so that it can inferencemostly in German as another language of meaning representation (e.g.besides English). It is to be understood that the system may switch fromtime to time between the language drive semantics in order to inferenceon structures that lack in one representation but are present in anotherand thus achieving multi-lingual, multi-custom, multi-domain andmulti-hierarchy inference coverage. The system may infer and/or usemulti-language and/or multi-cultural capabilities of collaborativefluxes (e.g. monocultural, multicultural) and/or associated factors.

The system may maintain particular semantic artifacts for particularcontexts. In an example, semantic artifacts associated with a drivesemantic of BEST FRIENDS FROM SCHOOL may have associated slang and/orparticular rules and artifacts that drive semantic inference andnarrative in a particular way.

The semantics may be associated with patterns, waveforms, chirps.

The semantics may be associated with parameters, inputs, outputs andother signals.

In an example semantics are associated with a parameter identifier (e.g.name) and further with its values and intervals, potentially via asemantic group.

The semantic factors may represent quantitative indicators associated tosemantics.

The semantic system may use caching techniques using at least one viewframe region and/or structure to store semantics. In semanticexpiration, the semantics may expire once the system infers othersemantics; that might happen due generalization, abstraction, crossdomain inference, particularization, invalidation, superseding,conclusion, time elapse or any other process that is represented in thesemantic model. Processes like these are implemented through theinterpretation of the semantic model and semantic rules by the semanticengine and further semantic analysis. The semantic inference may usesemantic linguistic relations including semantic shift, entailment,synonymy, antonymy, hypernymy, hyponymy, meronymy, holonomy, polysemy.

Semantic techniques and interdependencies may be modeled within theinference models and semantic rules. In some examples polysemy ismodeled via semantic composition where the meaning of a polyseme isinferred based on the compositional chain. Further, semantic groups,semantic rules and semantic models may be used to represent semanticdependencies and techniques.

Semantic techniques may be implemented via semantic models includingsemantic attributes and semantic groups. In an example, a semantic groupcontaining all the synonyms for “great” is stored. In some cases, thegroup comprises semantic factors assigned to semantic components toexpress the similarity within a group or with the semantic attributesdefining the group.

In both semantic flux and semantic streams, the source of informationmay be assigned semantic factors (e.g. associated with risk) and as suchthe inference by a system that consume semantic information from thesource may be influenced by those factors. More so, the factors can alsobe assigned to particular semantics, type of semantics (e.g. viasemantic attributes), themes and so forth that can be found in thefluxes and streams. Semantic fluxes and streams may be represented asidentifiers and/or semantics (e.g. based on annotating them inparticular or in general based on a characteristic by a user) and/or beorganized in semantic groups as all the other artifacts.

The system may use semantic time management (e.g. rules, plans etc.) tomanage the semantic factors for the semantic fluxes and streams.

It is therefore important that the information from various semanticsources including fluxes, streams, internal, external be fused in a wayto provide semantic inference based on the model at hand.

It is desirable that systems be easily integrated in order tocollaborate and achieve larger capabilities than just one system. Theadvantage of semantic systems is that the meanings of one systembehavior can be explained to a second collaborative system throughsemantic means. As such, if for example system A provides and interfaceand is coupled to system B through some means of communication then thesemantic coupling may consist in making system A operational andexplaining to system B what the meaning of the inputs/outputs fromsystem A in various instances is. The system B may use sensing andsemantic inference to infer the meaning of the received signal fromsystem A. Alternatively, or in addition the system A and B can have onecommon semantic point where the systems can explain to each other whatthe meaning of a certain input/output connection mean at some point. Forexample, if system A and system B are coupled through a common semanticpoint and also have other signaling and data exchange interfaces betweenthem then when a signal is sent from A to B on an interface, the commonsemantic point from A to B will explain the meaning of the signal from Ato B. In some cases, the systems A and B are coupled through a semanticstream wherein the common semantic point comprises the semantic flux. Assuch, the system B may use its own inference model to learn from theingested data from system A; further, the system B may send hisinterpretation (e.g. via model) back to A; the system B may just use thesemantic meaning provided by system A for interpreting that input/outputsignal/data or use it for processing its own semantic meaning based onsemantic inference, processing and learning techniques. In otherinstances, the system B will ask/challenge the system A about what themeaning of a signal is. In some cases, the semantic fluxes that connectA to B make sure that the semantics are requested on system B fromsystem A when their validity expire. The system B may be proactive insending those requests and the system A may memorize those requests insemantic routes groups and/or views and process them at the requiredtime. The system may use the semantic budgets for transmission throughthe semantic network and the semantics may expire in the network oncebudget is consumed.

In further examples, semantic group resonance may be applied for fasterlearning (e.g. of semantic groups and/or leadership), safety,communication and/or further inferencing.

In semantic group resonance, system A induces coherent inferences at B(e.g. affirmative toward the goals of B); further, system B inducescoherent inferences at A (e.g. affirmative towards the goals of A).Thus, semantic group resonance allows (continuous) coherent inferenceswith potential low/high (entanglement) entropy of A and B whileincreasing superposition. Semantic group resonance with low(entanglement) entropy is associated with affirmative factors;analogously, semantic group resonance with high (entanglement) entropyis associated with non-affirmative factors. Semantic group resonancefactors may be quantified in an example through low confusion,dissatisfaction, concern and/or stress factors between the members ofthe group and it may collapse when decoherence (e.g. high incoherence,confusion, dissatisfaction, concern and/or stress between the members ofthe group) occurs.

Semantic groups resonance determines and/or is associated with lowconfusion, dissatisfaction, concern and/or stress factors.

In semantic systems the semantic time between resonance and decoherencemay be used to infer coherent artifacts and/or operatingpoints/intervals. The system may learn causality (e.g. of resonance,decoherence) comprising semantic routes/trails, rules and/or othersemantic artifacts. In some examples the system infers DO/ALLOW rulesand/or further rules (e.g. time management/factorization/indexing etc.)when affirmative resonance occurs, and/or DO NOT/BLOCK rules and/orfurther rules when affirmative decoherence occurs. Analogously, thesystem infers DO NOT/BLOCK rules and/or further rules (e.g. timemanagement/factorization/indexing etc.) when non-affirmative resonanceoccurs, and/or DO/ALLOW rules and/or further rules when non-affirmativedecoherence occurs. Further, damping may be learned by the system; assuch, indexing and/or decaying factors and further rules may be learnedbased on resonance and/or decoherence (factors) and be associated withdamping semantic artifacts.

In some examples, the system learns damping factors and/or rules withinthe semantic mesh associated with the absorption and scattering ofelectromagnetic radiation in elements and/or (semantic) group ofelements.

Damping rules and artifacts are used to infer hysteresis and vice versa.They may be used for adjusting factors, budgets and or quanta in orderto control the damping towards goals and/or keep (goal) semanticinference within a semantic interval. Damping rules may be used forexample to control the damping components (e.g. of shocks,electromechanical dampers etc.) of a drivetrain (e.g. of posts, vehiclesetc.).

In some examples, system A uses semantic artifacts associated withsystem B (e.g. (portions of) semantic trails, routes, rules, drives,goals and/or orientations etc.) to induce coherent and/or resonantinferences at B and/or reduce confusion at B; this pattern may associateA as a (group) leader.

Semantic resonance is high for coherent semantic groups (e.g. theresonant inference in the group does not incoherently collapse).Semantic resonance is low for incoherent semantic groups and/or lowcoherency semantic groups. The system may infer highly coherentcomposite goals for coherent semantic groups. The system may useprojected resonance on (target) artifacts (e.g. flux, user, patientetc.) and/or groups thereof in order to diffuse, attract, group,increase positiveness and/or to decrease dissatisfaction, concern,stress etc.

Projected resonance between (high entanglement entropy) semantic groupsmay be used to learn damping, hysteresis and/or further rules.

Model and sub-model distribution/exchange may occur between system A andB. This exchange may be controlled (e.g. allowed, blocked, blurredand/or diffused) via semantic access control and gating. In an exampleparticular semantics and/or associated semantic artifacts are blocked.In another example, semantic groups related to MRI EXAMS may be blurred;while the system may blur the entity/object groups (e.g. patients,images, patient-images etc.), other semantic groups (e.g. related withlanguage interpretation) may be allowed to pass; alternatively, or inaddition, the system may use semantic diffusion in order to conveyinformation in a controlled fashion. In other example the semanticgating is based on semantic budgeting inference and/or speculativeinference. Thus, a semantic flux B might expose to flux A the semantics(e.g. potentially marked semantics) and the semantic capabilitiespotentially with estimated budgets and the flux A performs semanticinference on gated semantics and flux B exposed semantics. If thesemantic inference doesn't meet required budgets, then the system A maychoose to filter or reroute the semantics that do not meet therequirements. Entity and language filtering and semantic gating may becombined in any way to allow/deny transfer of information betweensystems.

In general, two communicating systems may use explanatory protocolsand/or interfaces; as such, a memory conveyed through a first mean isexplained and/or reinforced through another mean.

The system B may maintain semantics from A and the system keeps semanticfactors associated with them that may decay in time. Sometimes, thesystem B sends the requests to system A when the factors decay, reach aspecific threshold and/or based on semantic budgets.

In many computer systems data is exchanged via objects, sometimesrepresented in JSON or other object streaming formats. The exchangeddata is interpreted based on a static interpretation of JSON objectproperties or based on JSON schema parsing.

The interfaces may be statically coupled, and the operations and/orfunctions established a-priori and/or they may be encoded/explained in adynamic way in the JSON objects (e.g. one field explains another throughsemantic means such as semantic augmentation, synonym and/or antonym.These interfaces are not very adaptive due to semi-rigid implementationof the coupling between the systems.

An adaptive approach of communication learning may involve a system Blearning at first from a system A about the data is conveying andupdating its semantic model in order to be able to infer semantics basedon that data. In some examples, the system B learns a new language basedon learning interfaces. In such an example, the learning interfacerelies on common system A and B observations (e.g. sensing, semanticwave) and potentially basic rules and models for inference learning.

The implementation of interface learning may be achieved via a semanticpoint where the interface is described via a language or semantic wave.Alternatively, or additionally the semantics of the interface and therelationships can be modeled via a tool that will generate a semanticplug-in model for the interpretation of the interface inputs. Thesemantic tool and/or plug-in allows the description of the interfacebased on semantic rules including management rules. The plug-in modelmay then be deployed to the connected systems and the connected systemsuse it for semantic connection. The plug-in model may be deployed aspart of a separate block circuit and/or semantic unit that connects thesystems. Alternatively, or in addition, the plugin may be deployed in amemory (e.g. flash, ROM, RAM etc.). Further, the plugin modules maycomprise encryption capabilities and units whether semantic or not. Insome examples the plugin modules are used to encrypt and/or modulatesemantic waves. The encryption and/or modulation can be pursued in anyorder using semantic analysis techniques.

The semantic connection (e.g. semantic flux) may be controlled through asemantic gate that allow controlled ingestion or output of information,data and/or signals through semantic fluxes and/or semantic streams.

In FIGS. 16 and 20 we multiple elements (e.g. semantic units) coupledthrough links/semantic fluxes. As illustrated in FIG. 16 , a pluralityof elements (semantic units) are labeled with letters A through W. Eachof the elements may comprise computing and/or memory components. FIG. 16further depicts semantic groups of elements in a hierarchical structure(e.g. Group 1:1 (which is defined by the perimeter formed byG-H-I-J-K-L), 1:2 (formed by elements A B C D E F), 1:3 (formed byelements M-N-P-O), 1:4 (formed by N-V-W-O) at level 1; Group 2:1 (formedby N V U T S-R-Q-O, further indicted by thicker connecting perimeterline), 2:2 (indicated by thicker connection line joining A-F-G-H-I-J) atlevel 2); it is to be understood that while only two hierarchical levelsare depicted, more levels may be present.

In some examples semantic fluxes and/or semantic streams are ingested bysystems and possibly interpreted and/or routed based on semanticanalysis. FIG. 20 illustrates one example, and as discussed furtherbelow a plurality of semantic units may be arranged such as semanticunits SU1 through SU9. One or more external signals, e.g. 68 a, 68 b maybe received by one or more of the semantic units. The semantic units arelinked to one another in a mesh through semantic flux links, e.g., L1through L19.

The semantic gate may filter the semantics in exchanges. The semanticgate may be controlled and/or represented by a set of access control,time management, rating, weighting, reward and other factor rulescollectively named semantic management rules; access control, timemanagement, rating, weighting and reward rules are comprised in patentpublication number 20140375430. As such, the semantic gate may allowadaptive control of the exchange of information anywhere between a veryfixed controlled environment and a highly dynamic adaptive environment.The semantic gate may contain rules that block, allow or control theingestion of particular semantic artifacts based on access controlrules. The endpoints of a semantic flux (e.g. source and destination)may be represented in a hierarchical semantic network graph and thesemantic flux being associated with links in the graph. The source anddestination may be associated with semantics and the semantic gatecontrol rules are specified based on these semantics; in an example,such semantics are associated with activities and/or locations and theymay be collaboratively or non-collaboratively semantically inferred.Such semantics may be assigned to various artifacts manually, throughsemantic inference, through authentication or a combination of theformer.

We mentioned the use of hierarchical semantic network graphs for meaningrepresentation. The semantic gate may be used to control the informationflow between any of the elements of the graph and/or betweenhierarchies. The graph elements and hierarchies are associated withsemantics and as such the semantic gate controls the semantic flow basedon such semantics.

In an example, the access between hierarchies is based on access controlrules; as explained above the hierarchies may be associated withsemantics and/or be identified by semantics. Further, access controlrules may be associated with semantic identities and/or furtheridentification and authentication techniques. In some examples, theidentification and authentication are based on semantic analysis and/orsensing comprising data ingestion, image/rendering/display capture,radio frequency, electromagnetic modalities and/or othermodalities/techniques.

Information flows within and/or between semantic network model artifactsare controlled based on semantic gating. In some examples, informationtransfer flow between linked endpoints mapped to display interfaceareas, semantic groups and/or user interface controls is enforced thisway. In further examples, the gating is coupled and/or based on thehierarchical inference within the semantic network model and/or semanticviews which provide contextual localization pattern, access control andsemantic intelligence pattern of the mapped areas, semantic groupsand/or user interface controls. The mapped areas may comprise forexample displayed text, user interface artifacts, controls, shapes,objects and/or a combination thereof; also, they may comprise and/or beassociated semantic groups, semantic identities and/or patterns ofdisplayed text, user interface controls, shapes, objects and/or acombination thereof. Thus, the system may create groups, use fluxesand/or allow the flow and/or assignment of information from one mappedartifact to the other only if the semantic gating would allow it. Infurther examples, the system performs projected compositional semanticanalysis on the semantics assigned to the linked artifacts and based onthe projected analysis perform the semantic gating.

Linked semantic artifacts may be inferred based on semantic analysis. Inan example the system infers the purpose and/or goal of artifacts and/orsemantic groups in at least one semantic identified area (e.g. window)and may link such artifacts based on similarity of purpose, goal and/orfurther inference. It is to be understood that the linked artifacts maybe inferred and/or mapped by selecting, dragging and/or overlaying thesemantic areas and/or mapped artifacts on top of each other via any I/O(e.g. touch interface, screen, pointing device etc.); further, in someexamples the system provides feedback on such operations (e.g. deny theoperation, inform the user, pop up an image control and so on). Infurther examples, semantic groups of artifacts are created by selecting,dragging and/or overlaying the semantic areas and/or mapped artifacts ontop of each other and the user is prompted with selecting and/orconfirming the (composite) semantic artifacts (e.g. semantics, semanticgating rules, semantic routes, profiles and/or further artifacts) forsuch semantic groups (e.g. between the group members or with groupexternal artifacts).

A received input may not be ingested or partially ingested if thesemantic engine infers a semantic that is forbidden by the semanticgate. A partial semantic determination occurs when some of the semanticsare partially inferred on a partial analysis of a semantic route, goaland/or budget; sometimes those semantics are discarded and/orinvalidated. However, other times those semantics may not be discardedor invalidated; instead they may be assigned a factor and/or time ofexpiration or a combination of those. Such partial inference may beuseful for example in transfer inference and learning. In some examplessemantic trails and/or routes associated with semantics in a domain maybe partially applied and/or associated to semantic artifacts in otherdomains based on higher hierarchy inference on the semantic model.Decaying and semantic expiration may be used for controlling a semanticgate. The semantic analysis may be used to update the semantic factorsand time management and update the dynamic of semantic gates.

The semantic gates may be plugged in to the semantic analysis and/orutilize semantic network models where endpoints represent the source (ora source group) and destination (or a destination group) of semanticfluxes. Source groups and destination groups are represented as semanticgroups.

A semantic group consists of at least two entities each being monitoredin the semantic field that share a semantic relation or commonality viaa semantic (e.g. semantic attribute). A semantic group can be semanticdependent when a semantic attribute is assigned to specify a dependencyor causality relationship within the group (e.g. A INFECTED B, JOHNPERFORMED MRI_EXAM) or, semantic independent when there is no apparentrelationship between the objects other than a classification or a class(e.g. A and B are INFECTED systems). In further examples, A, B, MRI_EXAMmay be on their own assigned to semantic groups, for example for storingsignatures of viruses, images from MRI-EXAM etc.

It is to be understood that the causality relationships and learning maydepend on the semantic view and semantic view frames; further, they maydepend on semantic field orientation and/or anchoring. In an example,the observer's A semantic view sees the effect of the sensor blinding onB as a result of a laser or photon injection at a later time than thesystem's B semantic views detects such blinding effect. The inferencetime and/or propagation (and/or diffusion) may be circumstantialat/between A and B, and thus, while the order of those collapsedinferences may be more difficult to project, they may be considered asentangled from particular semantic views (e.g. of an observer C).Further, systems' projected inferences in regard toaction/command/observations might comprise a high degree of certainty inrelation with semantic artifacts which may be used as anchors forsemantic orientation. For observer's A semantic view, the cause of theattack was that system B is a “slacker flimsy protected” while forsystem's B semantic view the cause of the attack was because “A is abully”. Thus, causality relationship may comprise additional informationat a (hierarchical) level associated with the two entities (e.g. a linkfrom A to B “sent malware because it is a slacker” and a link from B toA “this is a bully who's probing me”, “this is a bully who infected me”etc.). While at a different level and/or semantic view, of A, B and/or athird observer C, the causality specifies the cause effect of A INFECTEDB; it is to be understood that this higher causality may be comprised,inferred, acknowledged and/or represented only for particular viewsand/or observers (e.g. B might not acknowledge or infer that it has beeninfected by A probing). It is to be understood that the cause-effectrelationship (e.g. infected “because” is a bully) may be modeled asoriented links and used to explain “why” type questions (e.g. why Ainfected B?—because A is a 80% bully and B is a 70% little 20% flimsyslacker; why is A a bully?—because it infected B and C and D and I 100%think is wrong). In further examples, the propagation and/or diffusionbetween a first and a second endpoint is based on assessing the semanticdrift and/or shift of/between the semantic artifacts associated with theendpoints; thus, the system may infer propagation and/or diffusionsemantic rules (e.g. time management, access control, indexing,factoring etc.).

Semantic anchoring allows the system to determine a baseline forinference (e.g. an observed object, high factorized artifacts, semanticgroups, semantic identities, themes of interest etc.). The anchoring maybe based on a collection of artifacts and the system uses projectedinference and semantic analysis based on such anchors. Further, theanchoring semantic artifacts may be determined by mapping and/oroverlaying a semantic network sub-model, layer, shape, and/or templateto a semantic network model (e.g. based on similar semantic basedartifacts, artifacts with particular semantics—e.g. goal based, antonym,synonym, orientation based etc.—in both the base and the overlaidnetwork model). The anchors may map and/or project into varioushierarchies, semantic views and/or frames. Anchoring may expire based onsemantic analysis; once the anchors expire the system may invalidatecorresponding semantic views, frames and/or regions. Semantic anchorsmay be inferred based on leadership inference; further semanticdiffusion and/or indexing may be used to expand or contract the anchors.

Semantic anchoring, drifts and/or indexing may change based on theorientation and/or intensity of the gravitational field within and/orassociated with the semantic field and/or endpoint. In further examplesthe semantic field is a higher hierarchical endpoint associated and/orcomprising particular gravitational fields. Semantic drifts may beinferred and/or associated with gravitational fields/waves and/orvice-versa; further, they may be associated with semantic timemanagement. Semantic anchoring may be indexed and/or change based onsemantic drifts, semantic fields (and/or endpoints), gravitationalfields and/or waves. In some examples the gravitational fields and/orwaves are inferred using semantic sensing analysis.

In some examples the system represents the semantic groups in thesemantic network model. In some example's entities are stored asendpoints and relationships between entities are stored as links. Thesystem may create, activate, block, invalidate, expire, delete endpointsand links in the semantic network model based on semantic analysis andsemantic group inference.

The system may use specific hierarchical levels to represent semanticgroups of specific and/or leader semantic artifacts.

During semantic inference the system may activate various hierarchicallevels in the semantic network model based on semantic analysis, driveand leadership semantics.

A semantic gate may control the flux between sources and destinations. Asemantic flux is an oriented flow which may be assigned to an orientedlink.

A semantic gate and a semantic flux may be identified by at least oneother semantic artifact (e.g. semantic).

Additionally, if the semantic gating detects or infers a semantic thatis not allowed then the semantic gating may update the semantic modeland management rules (e.g. collapse the semantic route and associate thecollapsed semantic to a semantic rule). In an example, if the systeminterprets an input (e.g. semantic) from a particular flux as beingquestionable maybe because it doesn't fit the semantic inference and/ortheme of the semantic flux, the system may discard and reroute thesemantic artifact, update/create a semantic rule (e.g. for source,factors); it also may infer additional semantics (e.g. associated withcyber security features for example). In other examples the system asksfor feedback from a user or from other semantic hierarchies, domainsand/or themes; in some examples it may use further semantic analysis ofthe semantic before feedback request (e.g. synonymy, antonymy etc.). Inan example, a semantic unit may ask a semantic flux cloud if aparticular cyber physical entity is associated with HAZARD and/or, inother examples if the entity is associated with POISONED WATER. Thus,the system may search or provide inference on semantic areas, domainsand/or groups associated with semantic routes of HAZARDOUS POISON WATERand/or POISON WATER and/or HAZARDOUS WATER and/or HAZARDOUS POISONand/or further combinations of the semantics in the semantic route.

At a hardware level the interface between various components can beachieved in in a semantic way. As such the connection points and/orsignals transmitted between various components can be semanticallyanalyzed and/or gated.

A semantic gate may be represented as a circuit or component. As such,the semantic gate controls the signals received and/or transmittedbetween semantic components. A semantic gate may allow only specificsemantics/artifacts/themes/signals to pass through.

Semantic gating and flux signaling may be achieved by diffusiveprocesses. Further quantum tunneling phenomena may be used.

A semantic cyber security component deployed on a hardware layout may beable to infer, identity, deter and block threats. Further, by beingconnected to a semantic flux infrastructure and/or cloud is able tochallenge (or ask for feedback) on particular cyber physical systems,semantics, semantic groups etc. and perform access control based on suchinformation. It is to be understood that instead of challenging orasking for feedback about a particular cyber-physical systemalternatively, or in addition, it may ask for feedback about a semanticand/or semantic group associated with the cyber physical system.

In some examples the system may detect that the inferences related withat least one collaborator and/or semantic group determine incoherentsuperposition. Thus, the system may ask for feedback from othercollaborators and/or semantic groups; the system may prefer feedbackfrom entangled and/or conjugate collaborators and/or semantic groups(e.g. having particular entanglement entropies of composite semanticanalysis). Further, the system may decay specific factors and/orsemantics associated with the collaborators who determine, cause and/orinfer incoherent superposition and/or high confusion.

Signal conditioning represents an important step in being able toeliminate noise and improve signal accuracy. As such, performing signalconditioning based on semantic analysis is of outmost importance insemantic systems.

The semantic conditioning means that semantics inferred based onreceived measurements and data including the waveforms, parameters,envelopes, values, components and/or units are processed and augmentedby semantic analysis. Semantic signal conditioning uses semanticconditioning on unconditioned measurements and signals. Semantic signalconditioning also uses semantic conditioning to compose and/or gateconditioned and/or generated semantic waves and/or signals. Thus, thesystem is able to use semantic conditioning for a large variety ofpurposes including inference in a semantic mesh.

In an example, the system conditions a received signal based on amodulated semantic wave signal. The conditioning may take place in asemantic unit comprising a summing amplifier at the front end producinga composed and/or gated semantic wave signal. In an example, thecomposition and/or gating is performed by modulating the output signal(e.g. voltage) based on the input signals (e.g. unconditioned signals64, conditioned and/or generated semantic wave signals 65) to be added(as depicted in FIG. 19 A B C). It is to be understood that theamplifier GAIN Rf 66, SU GAIN 67 may be also be adjusted based onsemantic artifacts (e.g. semantics, semantic waves etc.) and/or be initself a semantic unit (SU GAIN); adjustments of the gain may be usedfor access control and/or gating purposes in some examples wherein theoutput voltage may be adjusted to account for allowable transitionsand/or semantics. While an amplifier has been used in examples, it is tobe understood that in other examples additional and/or alternativeanalog and/or digital voltage adders, operational amplifiers,differential amplifiers, analog blocks, digital blocks, filters and/orother components (e.g. as specified throughout this application) may beused. Also, while the depicted examples may show physical and/or logicalelectronic components and/or blocks including capacitors, resistor,amplifiers, inductors, transistors, diodes and other electronicparts/units/blocks, it is to be understood that they may not be presentin other embodiments or they may be substituted with other componentsand/or parts/units/blocks with similar or different functionality. In anexample, the capacitors C in FIG. 19 might be missing altogether;further the amplifier A may be missing and thus, the front-end blockmight be purely a signal adder. It is also to be understood that allresistances, capacitances, inductances and/or gain of components may beadjustable and the system may use semantic means (e.g. semanticmodulated signals) to adjust such values and/or control components.

The switching (e.g. as provided by MUX) and variable GAIN functionalitymay be semantically controlled and may be used to implement semanticrouting and/or gating. While in the depicted examples thosefunctionalities are implemented in discrete components and/or blocksthey may also be substituted and/or composed (e.g. physically; logicallyvia semantic grouping and analysis) with other components and/or blocksand provide similar composite functionality.

It is to be understood that the semantic unit inputs, outputs and/orgain units may be mapped to semantic fluxes and/or gates.

The system may use voltage and/or currents values to represent semanticartifacts. While some depicted examples use variable voltages formodulating semantic signals it is to be understood that alternatively,or in addition, variable currents values may be used to modulate suchsignals and/or represent semantic artifacts.

It is to be understood that such semantic units may be used in a mesh inorder to condition and/or analyze the signals potentially in a recursivemanner where the generated semantic waves signals are used asconditioning signals in the semantic mesh (e.g. mapped to a semanticnetwork model, semantic fluxes/gates mapped to semantic unitinputs/output/gain). The mapping of the mesh to elements and routing isperformed by semantic orientation and/or routing. The semantic waves maybe generated as explained throughout this application including thosereceived from other sources, generated on previous received data,measurements and/or conditioning and/or other domain semantic artifacts.

Semantic waves waveforms and signals are used and/or stored in thesystem to represent any semantic artifacts. In some examples, they areused for identification purposes of any semantic artifact. In furtherexamples, the identification may comprise any combination of particularidentification, semantics, semantic groups and/or other semanticartifacts.

The unconditioned signals may come from any entity including analogblocks, digital blocks, front ends, sensing elements, modulationelements, I/O elements or any other hardware element. In some examples,the unconditioned signals are based on AC currents from power lines.

The semantic system infers semantics on patterns and compositions. In anexample, the system detects the pattern for a sensed semantic (e.g.ingested via optical or sound sensing entities) which is coupled toanother pattern in a semantic view (e.g. image reconstruction pattern,artifact reconstruction or pattern based on semantic group of attributesetc.).

The semantic system may infer a semantic based on a partial signalpattern; the signal pattern may present some partial resemblance with apattern represented in the semantic system; the system may assign afactor to the new inferred semantic based on a correlation between theactual and resembled pattern. In an example, semantic waves may beanalyzed based on partial signal patterns. The system may use semanticanalysis including orientation and routing for pattern recognition andlearning.

Semantic wave signals are generated and/or modulated through semanticanalysis (e.g. composition).

In further examples, the semantic waves are modulated based on anidentification, signature and/or DNA of semantic units and/or gatesthrough which they are routed and pass through. In an example, anunconditioned signal originated from at least one sensor element ismodulated with the identification, signature and/or DNA of the endpointsand/or semantic units through which is routed, and it passes. It is tobe understood that the DNA may comprise semantic artifacts related withthe respective endpoints, semantic units, semantic groups and/orhierarchies. Thus, as the semantic wave is routed in the semanticnetwork the system is able to trace sequences and trails of semanticunits and/or their DNA and thus being able to perform semantic analysisand further routing.

The system may use sequences of semantic units to infer compositesemantics and modulate the semantic wave. In an example, if the signalpasses through a sequence of semantic units such as SU1, SU2 then thesystem may modulate the semantic wave with a composite signature (e.g.DNASEQSU1-Level1 DNASEQSU2-Level1) of those units, which, when routedthrough SU3 is identified and collapsed into a further compositesignature (DNASEQ3-Level2) which allow the unit SU3 to modulate and gatethe semantic wave based on the new composite signature. In someexamples, the unit SU3 is a border semantic unit between multiplesemantic stages and/or hierarchical levels (e.g. Level1 and Level2)and/or semantic stages and thus the collapsed signature (DNASEQ3-Level2)may be available, collapsible or inferred only at Level2 and/or beyondbut not at Level1. While the previous example uses a limited number ofunits and signatures it is to be understood that this may expand to amore complex semantic structure including more units, multiplehierarchical levels, semantic groups (e.g. of units, endpoints,sub-models and/or signatures etc.). Also, the term “signature” has beenused it is to be understood that the term may refer to DNA sequences,semantic artifacts, identification etc.

Endpoint DNA may be replicated with endpoint replication. In someexamples the inference at an endpoint is incoherent, confused,non-collapsible and/or not matching the endpoint DNA, capabilities, goaland/or purpose; thus, the system may replicate the endpoint togetherwith the DNA until the coherency and/or confusion of the goal and/orpurpose is restored. Alternatively, or in addition, the system may remapthe endpoint to endpoints (and/or groups thereof) with similar DNA. Itis understood that the endpoint may be replicated and/ormapped/re-mapped on an existing and/or new semantic unit. Thus, semanticidentities and/or further artifacts may be associated with DNAsignatures.

DNA signatures compose during endpoint fusion. DNA signatures may beused to establish and/or infer anchors.

DNA based techniques may be used with medical imaging sensors (e.g.based on vision sensors, modalities such as CT (computed tomography),MRI (magnetic resonance imaging), NM (nuclear medicine), US (ultrasound)etc.) and/or biological sensors in order to model, detect and/or performsemantic augmentation in medical diagnosis, exams, clinicals,prevention, emergency, operating rooms and other healthcare based usecases. In some examples such biological sensors are part of a semanticunit, module and/or post; in further examples, they are wearable (e.g.surgical gloves, (exo) wearables, braces, bands etc.).

The system may perform memory, semantic model and/or semantic unitsaccess control, gating, factorization, decaying, enablement,disablement, invalidation, expiration, pruning in order to isolate theuse of semantic artifacts at various hierarchical levels.

Semantic waves may comprise electromagnetic waves generated and/ormodulated through semantic analysis.

Semantic waves may be modulated, transmitted and received in variousenvironments and using various technologies including electromagnetic,radiative, non-radiative, wireless, wired, optical, electric etc.

For example, semantic waves can be modulated and/or transmitted based onthe electro-optic effect manifested by particular crystals which changethe refractive index based on applied voltages and currents and thusmodulating the signal by changing the wavelength of the light based onapplied voltages.

When building a phase modulator, one can benefit from the effect thatthe refractive index n of certain crystals such as lithium niobatedepends on the strength of the local electric field. If n is a functionof the strength of the field, then so is the speed and wavelength of thelight traveling through the crystal.

Thus, if a voltage is applied to the crystal, then the wavelength of thelight crossing the crystal is reduced and the phase of the exiting lightcan be controlled by choosing the adequate voltage.

Thus, if a voltage is applied to the crystal, then the wavelength of thelight crossing the crystal is reduced and the phase of the exiting lightcan be controlled by choosing the adequate voltage based on semanticanalysis.

Semantic waves may be used for semantic control of devices and/or analogblocks. In some examples the semantic waves are used for displaypurposes where the semantic wave is decoded at semantic display elementsand the semantics rendered on the screen (e.g. RED 10 GREEN 5 BLUE 8, H17 S 88 V 9). In other examples, the semantic wave is used in a scantype display unit where the semantic wave modulates scanning opticalcomponent for creating display artifacts; while the display artifactsmay be raster, alternatively, or in addition they may be modeled andmapped as a semantic model and potentially stored in a semantic memory.

The system modulates and stores display artifacts and scenes as semanticmodels. Such semantic models may be modulated as semantic waves. Thesystem may perform semantic scene interpretation, composition andrendering based on superposition of semantic models and inference atmultiple hierarchical levels.

The system may perform semantic wave conditioning and deconditioningwhen performing semantic scene interpretation, projections, compositionand rendering. While the rendering may take place on display units it isto be understood that it may take place as a memory renderings or otheranalog and digital renderings. Thus, the system is able to perform scenecomposition, rendering, projections and/or analysis at any time.

In further examples the renderings are relative to a perspectiveendpoint and/or link in the semantic space and the system performsorientation, factorization, indexing, analysis and/or rendering relativeto the perspective artifacts (e.g. from perspective endpoint to field,current endpoint to perspective endpoint, link orientation etc.);further, the renderings may be based on semantic routes and trajectoriescomprising perspective artifacts.

In some examples semantic waves are used for control plane purposesincluding pilot or control sequences. The use of turbo codes andlow-density parity check techniques for error correction is well knownin wireless communication. However, those techniques may require fastinterleavers and lookup tables for data encoding and decoding. In asemantic wave the data is encoded based on semantics and as such thesystem is able to understand the signal even in most adversarial jammingconditions by adapting to environment. Further, error correction andcyber safety controls may be incorporated in a hierarchical manner andthus allowing hierarchical and/or domain coherent inferences.

In some examples, semantic waves may be used to convey and/or transfersemantic network models and/or semantic rules. Semantic information ismapped to artifacts such a frame or an image. Semantic waves may begenerated by semantic network models and/or rules while conveying asemantic network model and/or rule. In a cascading semantic wave, modelsand rules are generated based on recursive semantic analysis on semanticwaves, models and rules and used for further generation of semanticwaves. In some examples, at least two semantic waves are composed whilethe waves are modulated based on the cascading learning. In someexamples cascading semantic waves, models and rules may be used inencryption and authentication schemes. Such schemes may be used forexample in semantic model encryption and authentication, memoryencryption, collaborative semantic authentication and validation andother applications. Such semantic techniques may be associated withwavelets (e.g. wavelet compression, wavelet encryption). In someexamples, the system reconstructs the frames and images using suchtechniques. The frames and images are reconstructed based on thesemantically encoded semantic network models conveying space, time,semantic attributes, hierarchy and other semantic artifacts. In asimilar way, frames and images are deconstructed and semanticallyencoded in semantic waves.

The semantic wave may travel over and between different networksencompassing various modulation and transport protocols. In someexamples, the semantic wave is wavelet compressed before beingtransferred using such protocols. The addressability within the semanticlayer and/or networks may be based on semantic identification.

The system may perform gating on artifacts in images and/or frames basedon semantic analysis. Further, it may generate artifacts inimages/frames based on semantic analysis. In an example, an accesscontrol rule on a semantic flux/gate may specify that it needs toinvalidate, hide or filter objects in the pass-through images/frames. Assuch, the system maps and/or identifies such objects in the semanticnetwork model and invalidate, hide or filter corresponding artifacts ofthe semantic model, potentially based on further semantic analysis. Thesemantic network model may be mapped based on a particular format of theimage/frame (e.g. semantic artifact compression based on specific orstandard formats); also, it may be mapped on a semantic waveform. Whilethis is the faster approach, other variants may perform the mapping andthe semantic analysis using semantic gating points and/or units.Further, the semantic gating functionality may be incorporated into anI/O, control, sound/speech and/or display unit that render inferredsemantics and/or semantic waves on a display and/or other sensorydevices (speech, touch, vibration etc.). In further examples the gatingrules are based on various semantic artifacts defining and/or guidingthe gating inference. Alternatively, or in addition, the system mayspecify semantics that would replace the gated semantics in the resultedsemantic waves or gated artifacts (e.g. images, frames, speech, signaletc.).

Semantic mapping, compression, semantic gating and/or semantic wavingmay be incorporated in devices whether they provide capture, recordings,feeds, display, renderings, I/O, sound, speech, touch, vibration.Further such techniques may be applicable to any analog and digitalinterfaces.

Although semantic waves might be modulated directly on or as a carrierwave, they may be transmitted through other mediums and interfaces (e.g.network) that require the modulation, encoding, segmentation etc.through their own communication protocols and communication links.

The system may fine-tune and adjust semantic factors and thresholds onsignal conditioning elements to determine or infer a path. The semanticconditioning may be associated with semantics related to signal elementsincluding waveforms, envelopes, amplitude, phase, frequency and so on;the conditioning may be also associated with various modulations,formulas, algorithms and transformations. As such, the semantic systemmay adapt to various conditions and situations.

The semantic conditioning can be achieved via signal comparison,correction, correlation, convolution, superposition of a generatedsignal based on the conditioning semantic elements or other comparisonsbased on transformations and translations as wavelet, Fourier, Taylorand others. Sometimes the semantic conditioning doesn't yield a goodrating/factor and as such the system may generate and/or storeadditional semantic conditioning elements and rules learned duringconditioning cycles.

The conditioning may be associated with inputs from other systems,sub-systems, sources and modules. Thus, the system computes the semanticsignal conditioning patterns or chips including the conditioningwaveform and timing based on collaborative and multi domainintelligence.

A conditioning waveform may be used in combination with a baselinewaveform or a semantic wave to allow the adaptation of the system indifferent contexts and improve the accuracy, resilience and signal tonoise. The conditioning waveforms may be organized and represented assemantic artifacts including semantic routes, semantic trails, semanticgroups, rules and so forth. When a semantic route is associated with asemantic network model it comprises a relative orientation and/or shapein a semantic network space. The system may perform semantic orientationand/or shaping inference based on semantic routing, the identificationof the network model artifacts (e.g. endpoints and links) in the shapeand/or semantics associated with these artifacts. The orientation may bein an example relative to other semantic routes or to semantic trails;in such an example the system may further perform semantic orientationinference based on the groups of routes/trails and associated semanticnetwork artifacts (e.g. endpoints, links and/or semantic groups thereof,common semantic artifacts, links between routes, semantics, semanticgroups, semantic waves etc.). Thus, the semantic orientation may beassociated with or used to determine relative or absolute semanticdrifts and shifts, semantic groups and semantic shapes. Absolutesemantic drifts may use an absolute baseline in rapport to a semanticnetwork space, semantic views, semantic view frames, semantic routes,semantic artifacts and/or a coordinate system.

The semantic system modulates/demodulates, filters and composes semanticwaves and signals based on goals. In an example, for an artisticcreation the goal may be of NEW COMPOSITION in a context of anenvironment which may generate a routes and drive semantics of AUTUMN,BROWN, FALLEN LEAVES, LATE, QUIET. In other examples, the NEWCOMPOSITION may not benefit from much contextual environmentalinformation and as such the system may pursue very general semanticroutes. In other examples, when the goals and indicators are too vague(e.g. the factors are too decayed) the system may ask for feedbackand/or infer biases. The feedback and/or bias may comprise semantics andfurther factors which may determine drive semantics, semantic routes andso on. As mentioned throughout the application the system may group suchbiases and drive semantics with semantic routes and semantic orientationbased on further factors and indicators of semantic inference (e.g.factors and indicators matching “belief” semantic routes or high-levelsemantic artifacts). Alternatively, or in addition to feedback thesystem may use semantic profiles. In case of increased superposition,the system may perform superposition reduction. In further examples thesystem may perform new 2D and/or 3D designs based on semantic analysisand projections. In an example, the user specifies the features that abicycle rim may have and not have, and the system infers semanticshaping, semantic attributes and rendering of the rim parts and designs.The system may perform the design of 3D bicycle components based onfurther semantic shaping and analysis inference.

Semantic orientation is related with semantic routing in a semanticnetwork model where routes are mapped to various artifacts andhierarchies in the model.

In similar ways that the system performs semantic orientation, it mayperform semantic artifact comparison and/or projections. In an example,semantic shapes comprising one or more semantic routes and/or trails arecompared allowing the system shape and object recognition. In furtherexamples the system uses at least two semantic routes to infer at leasttwo semantics for a shape and perform composition and fusion on those.For example, the system may infer for a shape BLACK BOX 10 and LUGGAGE 4and because there is a semantic route between BOX and LUGGAGE andbetween LUGGAGE and AIRPORT (e.g. the semantic associated with theendpoint where the observation occurs) then the system may infer BLACKLUGGAGE 7. Further, semantic view frames, views, models, sub-models,groups may be compared and/or projected based on semantic orientation.

A semantic shape comprises semantic artifacts in the semantic networkspace comprising the shape. The semantic shapes allow meaningdetermination and inference in the semantic network space comprisingsemantic network artifacts. In an example, the semantic shape comprisesall endpoints and/or links associated and/or defined with particularsemantic artifacts. Further, the semantic artifacts that define and/orare associated with the semantic shape may be semantics, semanticroutes, semantic groups, drive semantics, goal semantics, indexingsemantics and any other semantic artifact. Thus, a semantic shape may beinferred based on such semantic artifacts and semantic analysis in thesemantic network space. In further examples the system infers furthershape semantics based on the semantic analysis in the semantic shape. Asemantic shape may comprise adjacent, non-adjacent, linked or non-linkedsemantic network artifacts. In other examples a semantic shape comprisesendpoints, links and any combination of those etc. Further, semanticshapes can span multiple hierarchical layers.

It is to be understood that a semantic shape inference is not limited tovisual mapping modalities, but it may encompass other sensing types andmodalities (e.g. sound, tactile, pressure, radio frequency, piezo,capacitive, inductive, analog, digital, semantic flux, semantic streamand other signal modalities).

A semantic network shape space may resemble at least one layer of ahierarchical semantic network model with semantic shapes and linksbetween them.

Further, a semantic shape may represent a (linked) grouping of semanticartifacts (e.g. endpoints, links and/or semantic groups) in a potentialhierarchical manner. Semantic shapes may be mapped potentially tofields, data, graphics, images, frames, volumes, captures, renderings,meshes, fluxes, layouts, sensing and further artifacts used in semanticanalysis. The access to hierarchies and/or semantic shapes may be accesscontrolled. In other examples a semantic shape comprises at least onegroup of semantic artifacts comprised and/or defined by semantic routespotentially in a hierarchical manner; it is as such, that most of theinference techniques applicable to semantic routes and compositions asexplained throughout this application can be used in a similar way forsemantic shapes and/or to infer semantic shapes.

The system may pursue various semantic routes during semantic analysis.The system may semantically analyze the inference on multiple semanticroutes and determine semantic groups and inference rules based on theinference on those pursued routes. Further, the system may associatesemantic shapes with such routes, inferences, groups and/or rules. In anexample, the system uses a higher semantic route of “LOW CLEARANCE”“SHAPE 1” and another one “FAST” “HIGHWAY” and the system associates thelower semantic shaping routes within the semantic model to at least onesemantic group, drive semantic and/or shape of CAR and further, ifadditional related inference and/or feedback is available (e.g.inferring the brand logo, text, external input etc.) to a drive semanticand/or shape for DELOREAN. Thus, the system may use various routesand/or rules for inference and augments the factors for the inferredsemantics based on the semantic analysis on such routes. In someexamples different routes reinforce the factors of various semanticartifacts and thus a high-level semantic understanding is likely. Inother case different routes determine factors to spread, decay and benon-reinforceable and thus higher-level understanding is less likely. Ineither case the system may pursue other routes and what if scenarios inorder to achieve goals.

The semantic orientation and shaping may be based on semantics whetherassociated with semantic routes and/or semantic groups. The semanticorientation and shaping allows the driving of inference and selection ofinference routes and rules based on a subset of drive semanticartifacts. In an example the system selects drive semantic artifacts androutes associated with synonyms belonging to groups where the drivesemantic is a leader.

Semantic orientation and shaping uses semantic hierarchy for inference.In an example semantic groups of semantic model artifacts are groupedtogether in higher level hierarchy artifacts and the system performsorientation based also on the new hierarchy artifact. Semanticorientation is used to group semantic artifacts together. Artifacts aregrouped based on semantic orientation and drift. In a further examplethe semantic routes themselves may be grouped.

Semantic routing may comprise semantic orientation and profiling for asemantic trail.

The semantic routing is intrinsically connected to semantic orientationin semantic analysis; as such, when mentioning either one is to beunderstood that the other one may be implicitly involved. Semanticrouting and orientation may use semantic drift assessment.

Semantic orientation, shapes and semantic drifts may be used todetermine and categorize actions, behaviors, activities and so forth. Inan example the system uses orientation and inference towards an actionand/or command. In another example the system uses semantic orientationand semantic drifts to infer whether an inferred semantic is associatedwith an action, behavior and/or command.

Semantic routing, orientation, shaping, drifting and further semanticanalysis (e.g. hierarchical, semantic profiles, gated etc.) may be usedto assess if short term planning (e.g. comprising sub-goals timemanagement rules) and/or execution matches long term (strategic)planning (e.g. comprising high-level and/or composite goals timemanagement rules). While the shorter-term (e.g. fast decaying) goals mayincur larger drifts in relation with the strategic goals (e.g. based onfactorizations and/or budgeting) the longer term artifacts (e.g. slowerdecaying, higher level artifacts) may incur smaller goal drifts.

The system may project and/or assess/reassess a (strategic) goal basedon the projections and/or realization of sub-goals (and/or shorter term)goals. In some examples, if the realization of sub-goals proceeds withlittle semantic drift from projections the system may not alter the(strategic) goal and consider it achieved when all the sub-goalscomplete. However, if the semantic drift is large and/or sub-goals arenot met then, the system may infer alternate projections and/orsub-goals; alternatively, or in addition, it may adjust, decay and/orinvalidate the (strategic) goal. It is to be understood that thesub-goals may comprise shorter term goals which may be associated withsemantic time management rules. In some examples, the adjustment of thegoals/sub-goals is based on a lowest entanglement entropy, drifts,indexing and/or factorizations between the old and the newgoals/sub-goals and/or further semantic artifacts used in projections.Competing requirements (e.g. associated with various semantic profiles)for short-term and/or long-term planning may determine elevated driftsand/or confusion factors which may be decayed by further budgeting, fluxchallenges, semantic profiling, hierarchical and/or gated inference offactors and/or indicators and further semantic analysis.

The system may perform deep learning feature recognition (e.g. based onCNN, RNN, LSTM) on the semantic shape and fuse the features andattributes detected within the sematic inference.

Semantic network models use semantic gating for transferring informationfrom one semantic unit and layer to another.

In another example, the system may infer that a shape is a DOOR LATCHbased on the position relatively the door mapped semantic model which isat an endpoint that is high factorized for LATCH, LOCK semantics androutes. In a similar example the system recognizes NUMBER 9 on a BLACKSHAPE and associates the RAISED CONTOUR surrounding the number withBUTTON and further infer REMOTE CONTROL for the BLACK SHAPE;alternatively, or in addition the system may recognize REMOTE CONTROLfirst and subsequently NUMBER 9 and associates the RAISED CONTOURcomprising NUMBER 9 with BUTTON and further REMOTE-CONTROL BUTTON. Thus,the system performs system inference using a plurality of routes drivesemantics and hierarchy levels in the semantic model. It is understoodthat the system may use semantic identities moving together in thesemantic space (e.g. BLACK SHAPE and BUTTON moving together at the sametime in user's hand) to infer further semantic groups and/or identities(e.g. REMOTE CONTROL); thus, the system is able to infer and associatesemantic identities in context (e.g. REMOTE CONTROL, REMOTE CONTROLBUTTON, NUMBER 9 ON REMOTE CONTROL BUTTON etc.).

In further examples, the system infers and/or uses connection indicatorand/or factors. In an example, two endpoints and/or semantic shapes areassociated each with WHEELS; and the system may infer a semantic groupif the wheels are associated with similar and/or identical semantics,semantic routes, drives, orientations and/or groups within a semantictime. Alternatively, or in addition, the wheels may be comprised in aparticular area, endpoint and/or other artifact. In further examples,the wheels move together and the semantic drift of their behavior (e.g.as inferred based on associated semantic routes and/or semantic views)is within a (coherency) range and/or semantic analysis is coherent. Infurther examples, the wheels are comprised and/or mapped to a linkingendpoint and/or area (e.g. car chassis).

It is to be understood that the shapes and contours including numbersmay be inferred through any techniques specified in this applicationincluding but not limited to sematic analysis, deep learning, semanticsegmentation etc.

A conditioning waveform may be used as an encryption medium wherein theconditioning waveform is used to modulate the encryption of a compositedata signal or semantic wave in an adaptive way based on semanticanalysis.

The semantic engine may run on optimized semantic hardware. Suchhardware may include ASICs, SoCs, PSOCs and so on.

Sometimes, to optimize the hardware, a semantic system may performevaluation, simulation, testing and/or automation of placements ofcomponents on a substrate, PCB or wafer based on semantic analysisincluding semantic shaping. Thus, the semantic system may use a semanticnetwork model which has a set of endpoints mapped to locations of atleast one substrate, PCB or wafer and the system performs semanticinference based on the components and substrate capabilities (mapped tosemantic attributes); further the system may represent component heatingand its impacts via semantic models and semantic rules (e.g. heatsemantics mapped to endpoints, semantic time management); further,communication protocols are mapped to a semantic model and semanticstreams/fluxes. Thus, the system may model many aspects of the designincluding cyber, performance, interference, power consumption,interface, radiation, leakage, heating and, thus, the system is able todetermine the mapping of components/semantics/attributes to locationsbased on semantic inference and semantic network models. The system mayinfer/simulate the mapping of those components and use the configurationthat yields an optimized semantic model based on ratings, rewards,costs, risk or other factors and/or analyses as explained throughout theapplication. In addition, the system may seek particular orientations ofsemantic routes for coupling and access (e.g. memory access) and performanalysis based on those routes coupled with previously mentionedanalyses. The components may include any electronic components andcircuits, ICs, substrates, layers and so forth. The hierarchy of thesemantic network model may resemble the hierarchy of photolithographiclayer imprints and a photolithographic semantic automation engine usesthe semantic model to automate the process through actuation andhardware control. In similar ways, the semantic system may be used todetermine locations and automate any other processes including trafficcontrol, robotic manipulation, image processing or any other systemrequiring space, time, access control coordination.

The system may extract metadata from various inputs, data and signalsand assign semantics to it. Additionally, the system asks for feedbackfrom another semantic system; the request is submitted to the systemwith greatest rating in relation to the theme. The challenge/responsemechanism may be realized through semantic fluxes and be controlledthrough semantic gates and semantic rules.

Additionally, groups of systems can develop group capabilities based onthe explanation of the interfaces, where the groups and leadersdetermine affinities to each other based on semantic analysis.

The semantic model may be used to model equations or algorithms. Thesystem may update the equations and algorithms and apply the updatedartifacts to semantic inference and data processing. An equation andalgorithm may be associated with a composite semantic artifact,collection of semantics, semantic groups and/or semantic routes.

Sometimes sniffers, detectors and memory data may be used with semanticanalysis to infer and learn patterns, semantic artifacts (e.g.indicators, routes, groups) of usual or unusual behavior pursued bymalware. In a similar way, deep packet inspections and/or protocolsniffers/detectors may be used and the semantic analysis would beperformed on packet data and metadata in the protocols (e.g. source,destination, type of packet, packet sequence, flags, ports, offset, acketc.). Thus, the system is able to perform semantic inference related tocybersecurity by combining methods like these that detect maliciousbehavior with code execution, protocols or other cyber relatedartifacts.

The system may infer potential (attempt) (cyber) breaches if receivedand/or entered (e.g. by a user, operator, flux, group etc.)authentication information exhibit a high semantic drift and/or(entanglement) entropy in rapport with the current and/or historicallegitimate authentication information.

A semantic controller may be used to control various hardware and/orsoftware components based on inference.

In some examples the semantic controller controls a robotic arm.Further, the robotic arm 13 having an upper arm 13 a and lower arm 13 bas seen in FIG. 1 , which may be used for soldering and/or componentplacing on a substrate and/or board (e.g. PCB). Thus, the semanticcontroller accesses and performs the specific actions at the solderingand/or component locations based on sensing, mapped semantic models(e.g. to substrate, layer etc.) and semantic analysis.

The semantic controller may be on another system, computer, component,program, task or semantic unit. The component may include generalcomputing components, real time components, FPGAs, SOCs, ASICs or anyother general or specialized components capable of interpreting thesemantic model. Sometimes, the semantic controllers may be networkedtogether for improved knowledge sharing and synchronization. As such,the distributed processing system operates according with thedistributed semantic model. The distributed semantic model may beinterconnected, transferred and developed using many techniques somewhich are described in this disclosure including but not limited tosemantic flux, semantic gate, semantic streams etc.

The semantic controller may be used as a cybersecurity component in thesense that will allow the usage of the system's resources by the programbased on the semantic model and multi domain semantic analysis. In anexample, the semantic model may include preferred semantic routes, whileother semantic routes are deemed risky, hazardous or not allowed. Assuch, the system enforces the security of the system bycontrolling/denying access and taking actions for the inferred semanticsor semantic routes that are hazardous or not allowed. Semantics andfactors associated to access control rules can be used for inferring,allowing, controlling, prioritizing and notifying.

The semantic units may use blockchains for authenticating sources (e.g.data source, semantic flux, stream etc.).

The system may encrypt semantic waves based on key certificates (e.g.public, private) assigned to identities and/or semantic groups. Thus,key encryption may be used to encrypt information to semantic groupswherein semantic waves are encrypted based on a key for the group; theinfrastructure may be able to distribute the decrypt keys to particularsemantic groups.

In further examples of semantic encryption, a semantic wave is modulatedat a source based on inference at various levels of the hierarchicalstructure and further encryption; further, the wave may be collapsed inparticular ways and/or only partially by entities, groups, hierarchiesand/or levels based on their semantic coverage. In some examples, thewave is not collapsible at some units, groups, hierarchies and/orlevels.

The semantic unit may be coupled with a semantic authentication andencryption system based on biometric data, certificates, TPMs (trustedplatform modules), sensorial, password, location and/or blockchain. Insome examples, the semantic waves and/or components thereof are encodedwith the keys and/or data provided by the aforementioned methods and becollapsible by particular artifacts and/or hierarchies.

It is to be understood that the semantic encryption and decryption maybe based on semantic hierarchical inference wherein particularidentities, groups and/or keys are allowed access (e.g. via accesscontrol, gating) or are associated to particular hierarchies and/orsemantic artifacts.

Analogously, the system may perform composition and/or semantic collapsebased on the inference on multiple elements and/or artifacts wherein thesystem may use a determined entanglement entropy to infer the missingand/or erroneous artifacts.

The system may consider and/or project the order and/or time of collapseat different entities, fluxes and other artifacts based on semanticmodel, location, orientation, budgets, semantic factors and furthersemantic artifacts. Further, it may couple such inferences with its ownbudgets.

A memory used by a communication or transfer module (e.g. network card,RF, optical module etc.) can be selectively transferred to othersystems; the data transfer is optimized and the data rate may increaseif the transfer is being shared between multiple transmit and/or receivechannels. In an example, wavelets compressed artifacts may betransferred in parallel or may be transferred selectively with variousresolutions and speeds based on semantic inference based on metadata; assuch, in an example, the image may be transferred at a base, adequate orrequired resolution at first and then being built at a higher resolutionbased on other streams. Alternatively, or in addition, for increasingreliability the system may transfer interleaved information based onvarious channels, fluxes, routes and semantic groups thereof.

A block of memory may be associated with a semantic identifier and thesystem infers semantics for the identifier and applies semantic rules;the semantic system may use semantic analysis to control the access tothe memory for I/O operations, transferring and/or receiving frommemory. Analogously with the access control on block of memories thesystem may perform access to web, collaboration, social, sites,messages, postings, display control artifacts, database artifacts, textartifacts, word processor artifacts, spreadsheet artifacts and so on.

In a semantic flux and/or stream scenario, the transfer rates in such amodule comprising a memory may look as follows. The sender has semanticmemory and/or buffers that need to be transferred. The sender pushes thedata and the semantic information associated with it to the memory andthe system decides which data to transfer based on semantic analysis;the system may adjust the communication and transfer protocol parametersbased on the quality of service and/or semantics (e.g. the quality ofservice may be modeled as a semantic; LOW, MEDIUM, HIGH, IMMEDIATE,potentially based on an input from a user). The system may use semanticfluxes and/or streams for transfer to/from memories. A semanticcomputing system may comprise a grouping of memories connected viasemantic fluxes and semantic streams controlled through semantic gates.The memory may be a semantic memory organized as a hierarchical semanticnetwork model and as such the level of access control, granularity (e.g.semantic resolution) in semantic inference and representation isincreased. The information is clustered based on internal semanticrepresentation for optimal access and performance.

In some examples the source has, obtains and/or determine semantics onthe data to be sent and the system uses the semantic information tointelligently send the data to the destination.

In an example, of a multimedia file (e.g. image, video) the sourcedetects artifacts in the data and infer semantics that are then used toselectively transfer data to the destination; further, the data may bemapped to semantic network models. The data transferred can be selecteddata, particular data, particular resolution data, particular componentdata, particular semantic data, particular hierarchical levels and anycombination thereof. The source system may selectively transfer the bulkof data since at first it sends the semantic interpretation of the datathat can be used by the destination for inference, access control andgating possibly based on semantic factors assigned to the source. Thedestination may reinforce the inference with its own semantic analysisof the received data. In an example the system sends a semantic fromsource to destination while preparing data for transfer (e.g. cached,buffered etc.).

The selectivity of data may be related for examples with selectedsemantics and/or factors (e.g. intervals). In some examples the systemmay selectively retrieve only portions of frames, images, videos and/orsemantic models based on risk, abnormality, semantic of interest fromPACS (picture archiving and communications system), EMR (electronicmedical record), VNA (vendor neutral archive) etc.; it is understoodthat in some cases the images, frames and/or zones of interest areannotated and thus the system maps semantic models to the annotated zoneand further perform semantic inference on the mapped annotated zone andon further mapped semantic models on zones comprised and/or comprisingthe annotated zone.

Once the destination reaches a satisfactory rating/weight or factor forthe semantic inference on the received semantics and/or data it may notrequire the remaining data to be transferred from the source and as suchit may inform the source of that aspects, let the transfer expire (via asemantic expiration) or block the transfer through access control (e.g.via semantic gating). Alternatively, or in addition, the source sendsonly o particular semantic scene from the original data together withits semantic interpretation and the destination assess the accuracyfactor (e.g. based on risk, rewards, cost etc.) of the semanticinterpretation in rapport with its own model; if the accuracy factormeets a goal (e.g. threshold and/or interval) then the destination mayaccept all the semantic interpretations of the source without furthersemantic analysis and/or further reception of the data; further, thistechnique may be applied on a sampling basis where the source sendssamples of the original data and semantic interpretation at semanticintervals of time.

In another example the destination may control the data transfer in thesense that it asks the source of particular data (e.g. data associatedwith particular semantic artifacts, resolutions, locations, imageregions, particular memory areas, particular endpoints, links,sub-models etc.) and the sender sends the data on demand. Thedestination may ask and/or be provided with access to various artifactsin memory based on semantic access control rules or other techniquesexplained in this application.

The system intelligently stores data on nodes. The distribution of datais based on localization, semantic and semantic rules. Further the datamay be distributed as a hierarchical semantic network model. As such,the system is able to map access the required data in a more effectivemanner. The mapping of the semantic models may comprise memory, blocks,devices and/or banks of the former.

For example, if a semantic management rule in a compute node specify asemantic or a semantic attribute in its rule then the semantic systemwill eventually cache the data at/for the node, the related objectsand/or semantic network artifacts that are potentially related and beaffected by that semantic; other objects may not be required and if thesystem detects unknown objects may automatically infer out of ordinaryevents and/or unknown events. Additionally, the system may furtherpursue semantic challenge/feedback to the node structure and/or feedbackfrom a user for finding more information about the subject.

In another example the system will selectively store parts of a largersemantic model based on the semantic rules at each semantic unit.

In an example, a semantic memory may be optimized for semantic inferenceand semantic sharing. Segments of memory may be mapped and/or associatedto endpoints and links; the memory links may be mapped and/or associatedto semantic fluxes and gates. The semantic memory may be segmented basedon semantics and the access control rules determine access to specificsemantics and/or memory segments. The system checks (e.g. challenges)the semantics, semantics, theme and semantic factors with another systemor component to see if is available and/or in what semantic budget (e.g.cost, semantic interval) will be; in some cases, parts of memory arebulk transferred between systems based on the semantics and themes ofinterest and access control rules.

Some of the semantic memory segments must stay unchanged while othersegments may be updatable based on various conditions including accesscontrol rules.

It is to be understood that when the connectivity between variouscomponents is not available and/or drops the system may pursueadditional semantic artifacts and/or routes based on the levels ofcoherence and/or confusion factors relative to interrupted semanticroutes, goals, views and/or other semantic artifacts. In addition, thesystem may preserve such interrupted inferences and further factorizeand/or decays associated factors (e.g. risk etc.) and/or associatedartifacts based on the reconnection time, delay, availability etc.; inan example the system factorizes the risk and/or cost based on theincreased channel incoherence. Further, the system may use thefactorization of risk to further factorize and/or index the decaying ofassociated artifacts; in an example the system may not decay theinferences occurred prior to a lost connection if the incoherence andthe risk factors of unfinished inferences is high.

In an example, a semantic autonomous system may contain a plurality ofsemantic memory segments with some segments that contain the hard-wiredrules having different access rules than segments which contain thecustomizable rules. The hard-wire rules may include general rules forsafe operation of the system and hence the access to change or updatethose rules are strictly controlled or even forbidden. The customizablerules on the other hand may be changed based on various factorsincluding local regulations, user preferences and so forth. As such, thecustomizable rules may be automatically updated by the system when itinfers a semantic based on location data and requires a new set of rulesassociated with those locations; other customizable rules may be also bedetermined, defined and/or customized by the user. In an example, anautonomous car roams from a legislative state to another which hasdifferent autonomous driving rules; as such, semantic modeled artifactsand rules (e.g. semantic routes, time management rules etc.) may beingested to comply with current regulations. Also, the car's semanticsystem may be modeled by a user providing guidance through varioussensing and actuation interfaces and the system determines semanticroutes based on those inputs. The system may infer, comprise and/oringest such customizable rules comprising time management rules. In anexample, the user specifies its preferences and/or priorities inparticular circumstances and/or activities and the system infers timequanta, the order and actual time for starting and stopping thesemantics associated with the circumstances (e.g. activities).

Optimized configuration may be also based on semantic groups andpossible semantics and/or locations.

In one example semantic identification command is used to identify asemantic group and the semantic group is configured with the optimizedconfiguration.

Semantic gate allows the control of the semantic information beingexchanged between various semantic entities. The semantic entities maybe organized in a hierarchical semantic network model and includememory, processing units etc. The access and the control of a semanticmemory used for data transfer is optimized for applying the semanticrules associated with the semantic gate (e.g. filtering and routing ofsemantics based on access control rules and/or semantic routes).

In an example of how a semantic memory may work, the system activatessemantic memory artifacts and semantics (e.g. memory associated withsemantic memory and marked semantics) which may stay active and/orreinforced until they are factorized, decayed, gated, invalidated and/orinactivated based on semantic analysis including time management. Assuch, next time when the system uses the memory for semantic inferenceonly the active and/or allowed inferences and semantics and/orassociated blocks or segments are valid and activated. The activation ofmemory may include electric voltage and current control, chemical,biological and DNA agents, other discrete and analog control whetherelectric or chemical in nature, biosensors, bio-transducers and others.

When the system infers a new semantic based on inputs (e.g. data,signal, waveform, value, pattern, etc.) or semantic analysis it issues arefresh challenge of the semantic analysis to the memory, correspondingmemory hierarchy level and/or select segments of memory based on thesemantic. The memory then refreshes the semantics, semantic model,reinforce/reevaluate/deactivate/expire the semantic together withassociated artifacts.

If the memory is hierarchical, the refresh of the semantic analysispropagation to various levels and stages may be based on semanticgating, semantic routing, semantic shaping, semantic factors, timemanagement, access control, and so forth.

The system may use hierarchical memory to store hierarchical semanticnetwork models. In an example, the memory hierarchy matches the semanticnetwork model hierarchy and potentially the access between hierarchiesis semantically controlled (e.g. through semantic gates, access controletc.). It is to be understood that while the hierarchy of memory isimplemented in hardware, alternatively, or in addition, it may bevirtualized thus abstracting hardware implementations. Thevirtualization may be based and comply with semantic views connect andsemantic gating requirements.

In some instances, the hierarchy of memory may be virtualized thusabstracting hardware implementations. The virtualization may be basedand comply with semantic views connect and gating requirements. Infurther examples, the virtualization may rely on semantic groups ofresources.

Memory caching processing and preemptive processing may be based onsemantics, on component semantic models, hierarchies and othertechniques as explained in the application.

The system may use semantic components and/or associative memory forimplementation of semantic memories.

In an example a semantic artifact and/or semantic identifier is activein a short-term memory (e.g. short-term semantic view) until it decays.Potentially, may be inactivated, expired, deleted and/or transferred toanother memory (e.g. recycle, longer term, higher level etc.) if itsfactor reaches a certain threshold/interval. The system uses semantictime management for structures of memory associated with semanticartifacts including view frames, views, routes and so on.

The system may generate or associate a particular semantic and/oridentifier with an access control rule; they can be associated with amemory block and/or with an entity or semantic group that require accessto the memory block. The access control rule may be associated withsemantic groups, possibly via a semantic attribute and other semanticidentifier. In an example, a semantic group comprises a memory blocksemantic identifier and an entity semantic identifier and as such thecomputer is able to control the access to the memory in a more facilemanner by associating access control rules to the semantic group.

The access to memory may be evaluated based on semantic analysisincluding synonymy, antonymy, meronym etc. The access may be alsoevaluated on causality semantics (oriented links and/or associatedendpoints and their related causality attributes etc.).

As specified above the management plans may include access control plansand rules. The access control rules are used to control access rights tovarious resources including memory and memory segments, disk and disksegments, networking and data transfer channels, sensors, controllersand any other hardware and software modules. It is to be understood thatthe resources (including memory) may be associated and/or organized as asemantic model with endpoints comprising segments, zones and linkscomprising channel and buses. By using such organization, the system mayincrease cybersecurity for example, by assigning risk factors tocommunication links and memory related endpoints and areas. Further, thesignal (e.g. semantic wave) routed and passing through such memory zonesmay be transformed and routed based on zones semantics.

A semantic sink may communicate with the semantic engine via a semanticgate. Any entity can incorporate the semantic sink and interact with thesemantic engine. The semantic engine performs semantic inference on thedata and signals received via a semantic sink; the semantic sink maycomprise a semantic flux and the semantic engine performs semanticanalysis based on the data and signals received via the semantic sinkflux. Thus, the semantic engine may be used to synchronize and/orcontrol the workflow in hardware and/or software components which embedor incorporate the sink on local or remote computer units and/or systemsand further for cybersecurity controls. The hardware components may beany components, devices, blocks and/or interfaces whether analog,digital, continuous or discrete.

A trail of semantics may be recorded based on a semantic route or adrive semantics whether inferred and/or specified by user. Sometimes asemantic gating is used for recording semantic trails.

The semantic model can be defined and configured locally for each systembased on user interfaces, provisioning, configuration management or datastores. The semantic model can be shared between various systems.Additionally, the semantic systems can share parts of the semanticmodels and potentially exchange semantic model updates in a way that ifone system is determined to have a better semantic model or parts ofthereof, be able to improve the other semantic systems models as well.

The system may use semantic gating for semantic model exchange.Sometimes the gating may be based on identifiers, names and so forth. Insome examples, the system uses gating for transmitting (or nottransmitting) and/or forwarding (or not forwarding) parts of thesemantic model that are associated with particular semantics and/orsemantic groups; in further examples the gating may be based on gatingdrive semantics where the system gates parts of the semantic model basedon the semantics associated with the gating drive semantics.

It is to be understood that the semantic model exchange may take placein a semantic network environment where a model in at least one endpointis gated to another endpoint.

In general, collaborative intelligence is superior to non-collaborativeintelligence. This is also associated with swarm intelligence and groupintelligence.

The collaborative intelligence may be materialized through distributedsemantic systems.

The semantic systems may be coupled through various semantic connectiontechniques and artifacts including semantic flux, semantic streams andsemantic gate.

Semantic systems may register and/or send advertisements with theirlevel or semantic knowledge and/or capabilities (e.g. themes, semantics,semantic factors, budgets etc.). Those advertisements or registrationsmay be based on location and space-time semantics in an example.Further, the registration may include operational rules, semanticroutes, parameters and other semantic artifacts. The receiving systemmay generate, and map semantic models and rules based on the registeredartifacts and locations of those artifacts.

Semantic systems may register with any semantic identity, potentiallybased on semantic profiles; further, those semantic identities maycomprise owner, installer, capabilities and so forth.

Semantic identification and/or semantic group may determine inference ofcapabilities and/or semantic attributes. In examples, the systemdetermines that the leadership semantic of a DELOREAN is the DRIVINGEXPERIENCE and thus in order to project improvements, increase ratingsand/or desirability of DELOREAN it may select goals which elevate theGOOD DRIVING EXPERIENCE related factors and/or decay the BAD DRIVINGEXPERIENCE related factors while allowing drifts of (inferred/projected)budgets based on risk projections (e.g. in rapport with competitionproducts, budgets, price etc.).

While semantic systems may advertise capabilities, further, semanticsystems may infer lack of capabilities in potential collaborators and/oradvertisers. The inference of the lack of capabilities may be inferredfor example on failed inference, incoherent inference, elevatedconfusion, projections, budgeting and/or further semantic analysis. Insome examples, systems that were not able to meet semantic artifacts,goals, projections, budgets, coherence, confusion and/or other factorsand budgets may be associated with semantic rules and routes whichreflect the decaying biases towards such artifacts.

While the preferred method of functionality comprises propagatingsemantics through the semantic connect once they occur, sometimes asemantic system (e.g. requestor) need to challenge or obtain informationabout particular semantic artifacts and themes. This may happen when thesemantic system is not connected a-priory to sources for thatsemantic/theme and/or the semantic/theme is not trusted or relativelydecayed (e.g. low weights, other low semantic factors,sub-thresholding); as such, the semantic system issues a challenge orrequest for information to the other collaborative systems(collaborators). Sometimes the response should meet a required semanticfactor/weight threshold and/or semantic budget. The semantic system mayspecify the required factor/weight level and/or budget to the requestpotentially through another semantic and/or semantic artifact.Alternatively, or in addition, the system may assess the bestcollaborative systems (including on an semantic group basis) that mayrespond to that request for information and ask and route only throughonly a selected few of collaborative systems for such information; theroute may be based on a semantic orientation. The selection of a systemmay be based on factors that an initiator holds about a collaborator.The requestor may determine the themes of interest and sends therequests to the selected collaborative systems that may provide the bestfactors for a particular orientation and budget. Alternatively, or inaddition, semantic flux/gates may expose and maintain semanticcapabilities with potential semantic budgets and the system uses thosefor semantic inference and orientation. Further, systems may maintainthose semantic flux/gate capabilities updated continuously based onsemantic analysis and/or similar requests, techniques in the semanticnetwork.

When external systems are using semantic flux/gate capabilities forsemantic inference it may rate the semantic flux/gate overall and/or inregard to those particular capabilities and/or associated themes.

The requestor may aggregate the received responses and usefactor/weighting rules to fuse the data from multiple semantic systems.The fusing of data may use any semantic analysis techniques for fusionincluding composition, route, trail, synonymy, antonymy, meronymy etc.

The system may determine the best components and collaborators based onsemantic orientation within the sub-model holding component andcollaborators capabilities and mapping.

Sometimes the collaborators process their factor for the informationthat they receive as a result of a challenge. Sometimes the response mayinclude the computed factor by the collaborator. The requestor may usethe received factor and its internal factor level of the particularcollaborators (e.g. general rating/risk or the rating/risk for theparticular drive semantic or theme) to compute an overall factor on theresponse. Further, the collaborator may provide semantic trails of therequested semantic artifact or inference to a requestor and therequestor uses such semantic trails to perform further semantic analysisand orientation.

The selection of collaborators can use similar techniques used forsemantic grouping, semantic identification, semantic routing, semanticmarking and/or inference.

The selection of the collaborators, authoritative semantic sources andthe routing to and through those systems may use semantics and/orsemantic techniques.

Inference on multiple semantic fluxes and/or groups determinesentanglement of inferred semantic artifacts. In some examples theinference system preserves an entanglement trail which may comprise thesemantic identities and/or DNA signatures of entangled semanticartifacts and/or contributors.

A semantic group may have leaders; sometimes the leaders areauthoritative for particular or on all semantics of a group. Theauthoritative qualification and/or level may be provided via semanticfactors. As such a requestor may decide or be forced by the semanticrules to route and obtain information only through a leader system (e.g.having a semantic factor for a semantic artifact that deems it as aleader). The leaders may be established based on ratings, weights orother semantic factors within the group related to particular semanticsand/or subjects.

The leaders may be the only ones in a group that publish gating and fluxsemantics related with their authoritative semantic artifacts. As such,they may be the ones that coordinate the couplings of units in the groupfor particular leader semantics and artifacts.

The leader type hierarchy may extend to the semantic network model whereparticular semantic network model artifacts or subject entities (e.g.master post) are leaders of a particular group, level and/or hierarchy.

Collaborative systems may not need to be directly connected in order tocollaborate. They may be dispersed in one semantic group or multiplesemantic groups. They can communicate via a cloud and/or meshnetworking. Such semantic groups may be represented by leaders forparticular semantics or in generalized manner; further the leaders mayconsist of semantic groups or partial leader groups within the grouphierarchy and any combination of the former. As such, the semanticintelligence and/or compute may reside on the cloud and/or nodes in adistributed manner. In an example such distributed intelligence is usedfor managing smart posts or autonomous robotic infrastructure.

The semantic distributed architecture comprises semantic groups and/orleaders at various levels within the architecture.

A semantic group of semantically related artifacts (e.g. meanings) mayhave an authoritative leader based on the particular contexts ofsemantic inference and/or analysis. A leader may comprise semanticartifacts such as component semantics, semantic groups, semantic routes,goals etc.

As mentioned, the semantic group formations may be based on semanticanalysis. As such, the semantic group formations and leadership arespace time, capabilities, context, objective and goal aware. Thesemantic group formations and leadership is based on artifacts in thesemantic network model, where semantic artifacts are inferred atdifferent levels of hierarchies. In a traffic management or smart postinfrastructure example the system defines semantic groups and leaders ina hierarchical manner on the larger areas (e.g. higher endpoints) basedon the semantics associated with such endpoints and endpoint hierarchyand, based on semantic analysis, defines groups and leaders within thehierarchy of semantic network model and semantic groups. While theprevious example mentioned larger areas and/or higher-level endpoints(e.g. based on more abstract or transfer semantics) it is to beunderstood that similar techniques apply to more granular areas andlower level endpoints. As previously mentioned, such inference can beassociated with any direction within the hierarchical structure.

The semantic systems can exchange semantics via semantic fluxes and thesemantic fusion consider them based on a factor/weight assigned to eachflux.

Semantic fusion takes in consideration the semantic model, semanticrules and semantic factoring for each composition when performing thefusion.

Further, the semantic fusion or composition may update the semanticfactors and semantic budgets of related semantic artifacts includingthose involved in fusion and composition. In an example, once the systeminfers a high factor composite semantic it may decrease or increase thesemantic factors associated to compositional semantics. Further thesystem may update the semantic budgets associated with selected semanticroutes. In an example a semantic time budget is updated based on theinferred semantic factor to reflect that the goal may be reached earlieror later than predicted. The system uses the semantic chain and semanticanalysis to update semantic artifacts. In some examples the system usesthe semantic chain and/or model of a semantic view and/or semantic viewframe for optimization.

A semantic view comprises and/or conveys semantic artifacts used and/orinferred by a semantic system and/or subsystem. A semantic view may beassociated with snapshots or frames of past, current and/or projectedsemantic analysis. A semantic frame view comprises a frame view based ona subset of semantic artifacts.

Semantic analysis may be performed on any type of data including text,binary, waveforms, patterns, images and so on. In an example, a semanticstream (e.g. based on images and/or frames in a video or spatialrendering) interpretation may correlate artifacts from various domains;further collaborative semantic image interpretations from varioussystems ensure multi domain knowledge fusion.

For example, if a system needs to infer how many people are cycling atone time, then the system might collect data from various fluxes andfusion, challenge (e.g. interrogate) and give priority and/or moreweight to those fluxes which provide data from areas where is daytime,assuming that less cycling is usually done overnight and that the systemhas inferred strong factored semantic artifacts (e.g. compositesemantics, semantic groups, semantic routes) based on semantic artifactsof cycling (e.g. cycling related semantics, semantic groups and semanticroutes whether based on semantic relationships of cycling and furthersemantic analysis) with daytime (e.g. based on semantic time). As such,the system may use semantic rules for semantic flux management includingsemantic routing.

The system may perform searching based on elements that are assigneddrive, route and/or leadership status in semantic inference. As such,the system is able to infer semantic groups and/or trails, renderingand/or storing those graphically, textually, binary and/or via semanticaugmentation.

A flux might be deemed more reliable (e.g. high reliability factor,lower risk factor etc.) than others in a particular semantic and/ortheme and hence is weight being adjusted accordingly.

In another example the trust and the semantic factors of semantic fluxesmay be determined based on the environment on which the semantic fluxprovider operates. If an RF and/or optical system operates in a highnoise environment, or on a contested or crowded environment then thesemantic determinations based on RF and/or optical sensing providedthrough the flux may be assigned semantic factors conveying high risk,hazard, low trust. Additionally, weights, ratings and semantic factorsof fluxes based on those determinations may be also affected.

Receivers may correlate information from different fluxes in order toassign semantic factors on fluxes and flux semantics. The semantic fluxmay be associated with semantics and/or semantic identifiers andparticipate in inference. The association may be based on externalinputs, inputs from a user, semantic inference and so on.

Templates and/or semantic rules comprising fluxes are used to developthe semantic system. A template or rule may specify that a flux may betaken in consideration for a particular semantic or theme based on itsfactor for that particular semantic or theme. Sometimes this is modeledthrough semantic gate and/or access control rules in which semantics aregated.

In an example, a semantic system may preserve the best, relevant orleader semantic fluxes for ingestion and semantic inference on variousthemes, semantics and/or goals. In another example a cyber securitysystem may assess and update the ratings of fluxes, themes, semanticsand such; it may ingest the low rated factor semantic artifacts anddetermine patterns of usage that determined the low ratings/factors andassign semantics to it. The cyber units and/or semantic engine usesaccess control rules to control access to resources. The resource may beany computer resource, hardware or software unit, analog or digitalblock or interface, component, device whether virtualized or not.

Sometimes the trust of a collaborator is based on vulnerabilitiesinformation processing in rapport with the collaborators capabilities orcharacteristics (e.g. modeled via semantic attributes) which may beimpacted/affected by such vulnerabilities.

The system might adjust its own semantic inference model, by fusingsemantic model artifacts received via fluxes into its own semanticmodel. E.g. if a factor of a flux is high on a particular semantic thenthe sub-model for that semantic might be updated with inferenceartifacts from the higher factorized system.

However, a semantic sub-model that functions well for a system might notfunction always that well for another system due to particularconditions and functional environment.

Hence it is critical to be able to assess the best model for each systemat any point in time.

Various smart sensors can capture various features and semantics with ahigh degree of certainty. Smart sensors may embed the semantic enginewithin an internal processing unit. Hence, the semantic analysis andsemantic fusion is closer to the sensor.

The semantic analysis and fusion may resemble a hierarchical approachbased on the hierarchies associated with the endpoints and/or links inthe semantic model. In an example, the system groups elements in thesemantic model based on semantic analysis (e.g. composition). In such away endpoints and/or links may be composed at any level of thehierarchy. In a similar way, semantic analysis may be based on groupingof semantic model artifacts. In an example, the grouping of endpointsmay be based and/or determine semantic composition on the semanticsassociated with the endpoints.

With semantic technology sensor fusion is more efficient and relevantmore so when there is a high degree of correlation between the data fromvarious sources. For example, infrared image/frame and an ultravioletvisual image frame in the same field of view may be correlated in orderto derive the types of objects in the image. Usually, the processing isoptimized if the two images can be superimposed or layered and/ortranslated to the same field of view, coordinate system and/or spatialnetwork models for coordinates matching. If the system based on sensorsoperating at various wavelengths (e.g. visible, infrared etc.) detect ashape of an unknown object in the visible spectrum and a heat shapesignature similar with that of a car in the infrared spectrum then thefused data associates the unknown object with a car based on overlayingand semantic analysis on the separate frames and overlaid frames. Insome examples the overlaying is achieved via separate hierarchiesassigned to the frames. Additionally, if other objects or artifacts aredetected in the semantic snapshot of the visible spectrum then thesemantic system might infer additional semantics once it inferred thesemantic for the unknown object and potentially control the sensor foroptimal sensing.

In a similar way, two or more semantic fluxes may feed in approximatelythe same semantic time interval information (potentially timestamped)related to an artifact in the semantic field (e.g. via messaging posts)and be able to fusion the inferences on the same theme, semantics and/orartifacts using semantic analysis. The system may be able to identifyobjects that artifacts are related to and the system associates theinferred semantics to it.

The information from two or more semantic fluxes may come from semanticgroups of systems based on semantic routes that determine the routingthrough such systems. Thus, the semantic fluxes allow the propagationsand semantic analysis through various semantic groups and by usingvarious semantic routes.

The semantic model comprises semantic templates and patterns. A semantictemplate and pattern might include factorization and time management.The template pattern and template may be associated with groups ofelements or semantic artifacts in the semantic model.

The semantic systems may use a particular language or symbology formeaning representation. The continuous development of the semanticmodels may potentially rely on language interfaces including speech,gesture and sign languages, text recognition, text inputs and such.

Additionally, semantics can be expressed or derived through these kindsof interfaces. In some cases, the interface relies on localizationtechniques to infer/convey meaning, where network model graphs may bemapped on the front-end sensing of such systems/elements to infer thesemantics of movement of artifacts from one location to another and/orfrom determining patterns of movement.

The proper syntactic formations are modeled through the semantic modeland semantic rules. The system may translate the language of meaningrepresentation to another particular language. As such, the artifacts ofthe language meaning representation may be associated with otherparticular languages via semantic relationships (e.g. semanticattributes, semantic groups, semantic rules etc.). Alternatively, oradditionally, the system may duplicate the meaning representation invarious languages for optimized processing (e.g. duplicate the semanticartifacts and relationships in two languages).

Syntax may be based on time management and rules templates in someexamples. Further, the semantic attributes may be associated to othersemantics in order to specify their characteristics (e.g. VERB, NOUNetc.).

As explained by U.S. Patent Publication No. 20140375430A1, which isincorporated by reference, the semantic attributes may be groupindependent or group dependent. The group independent semanticattributes may represent the type of object, the class of the members orother non-causal or non-dependent relationship (e.g. found in the samelocation or scene); the group dependent semantic attribute may signify acausality and/or the dependency of the objects in the semantic group. Inan example, the semantic system may use the semantic model anddeterminations to derive verbs. Verbs may be associated with thesemantic management rules. For example, the system may determine thetense of the verb by just examining the time of a semantic inferenceincluding examining a semantic trail and a semantic route; e.g. John andMary became friends may be derived just by examining the semantic trail,time, semantic time and/or semantic management rules for the semanticattribute “FRIENDS” associated with the semantic group (John, Mary); assuch, the system knows that the semantic attribute “FRIENDS” for thegroup has been inferred past the current semantic view frames and/orview and such it infers the past tense of the verb. Based on semantictime management and semantic composition the system may inferappropriate tenses for the verb and produce semantic augmentationoutputs.

In an example, the tenses are based on the distance in the semanticdetermination in a semantic trail. The distance may be based on time,semantic factors, indexing, semantics, semantic drifts and/or semanticinterval. Semantic factors decaying in a semantic trail can also beused.

Semantic indexing may be used to determine space-time distance,correlation and/or orientation in a semantic network model and forsemantic groups.

Sometimes the semantic systems convey meanings through language andsymbols which may be the same or different from the language of meaningrepresentation.

The particular language terms may comprise encryption, encoding andmodulation which are semantic based (e.g. generated based on semanticinference). In the reverse way, the translation from another language tothe main language of meaning representation may include decryption,decoding and demodulation.

The semantic model may learn representations from various sources basedon direct observations or by documentation of those sources and theirrepresentation rules. As such, any schemas may be described and/orunderstood.

The system may ingest data through various means including text,optical, pointing and touch interfaces. In case of optical, pointing ortouch ingestion the system may interpret inputs, locations, schemas ordrawings via mapping of the data and/or data renderings to endpointsand/or links in a semantic network model (e.g. semantic network graph).Other optical recognition techniques and deep neural networks may bealso employed. Optical recognition (e.g. shape, character) may be basedon a semantic network model mapping. The mapping between semantic modelartifacts and data and/or data renderings is based on a locationincluding a physical region, area, point location, shape whetherrelative to the data rendering, frame, image, captured environment,observer, relative position, global position or a combination of those.Actual locations or virtual locations may be mapped in such a way. Infurther examples the mapping is associated with locations in a frame orimage (e.g. pixels, segmented areas, objects, labeled or unlabeledregions, bounding box areas etc.).

Based on the use case the system may adjust inference and semanticmodels by information in semantic near and/or far fields. Based oninference of semantic near and/or far fields, the system mayhierarchically map, adjust and infer models and sub-models. Further thesystem may combine such operations with semantic gating.

The semantic mapping consists in mappings between data andrepresentation of the system with semantic artifacts of a semanticnetwork model.

Taxonomies and other vocabularies may be described and learned.

The efficiency of the semantic systems allows them to have the dataprocessed closer to a sensor element (e.g. on a microcontroller orspecialized circuit residing on the same PCB, MEMS, ASIC etc.), possiblyin a hierarchical fashion; this may increase the processing speed,operational capabilities and the efficiency of the operational decisionmaking. Some sensors on a chip may capture data related to variousparameters (e.g. acceleration, spin, gravity) in a high velocity fashionand the efficiency is increased by inferring semantics closer to thesensor itself on a processing unit (e.g. microcontroller, semantic unit)on a chip.

Therefore, it is important that the semantic model of the involvedsensor be available on the processing unit closer to the sensor.Additionally, the semantic engine on the chip might instruct the sensorto adjusts its settings based on the inferred semantics and/or receivedsemantics via semantic fluxes.

Semantics may be conveyed and/or inferred through speech/sound,visual/optical, touch, sensorial, signal and/or waveform, rf and anycombination thereof.

Semantic models ensure that the signal and data features are molded intoa human centric knowledge generation process.

The semantic model can include rules that are used for further expansionand adaptability of itself.

The semantic analysis comprises semantic techniques as synonymy,semantic reduction, semantic expansion, antonymy, polysemy and others.In an example, the user specifies semantic groups and/or providesemantic routes of synonyms, antonyms and other semantically relatedelements and inference rules. Elements in a group are by themselvesrelated via semantic attributes or semantics (e.g. SYNONIM, ANTONIM).Semantic reduction and/or expansion of groups and inferences may beachieved through semantic composition, semantic routes collapsing andother semantic representations and techniques. A user may specify thesemantic relationship via a pointing and/or touch interface; in such anexample terms are presented on a screen on a graph representation (e.g.chart, graph etc.) and the user drags one or multiple lines within therepresentation representing its semantic orientation perception betweenthe terms. Further if terms such as “quick”, “clever”, “fast”, “sharp”,“night”, “light” are presented in a chart the user may select atrajectory that resemble the precepted semantic drifts between suchwords. Further, if the operation is associated with at least onerepresentative (e.g. drive) semantic, the trajectory may resemble theprecepted semantic orientation in rapport with the at least onerepresentative semantic. Further, the system may create semantic groupsand semantic routes based on representative semantics and semantictrajectories in the semantic model. The distance of the selectedtrajectory to the semantics locations may be used to assess semanticorientations and drift.

A user may specify correction, goal and/or desired trajectories ondisplayed graphics (e.g. graphs, text, window and/or display controlsetc.); further, a user may specify interest points, areas and/orendpoints. The user may enter and/or the system infers semanticartifacts associated with such trajectories and/or endpoints. The systemmay define further endpoints at intersections of trajectories with thegraphic and perform inference comprising semantic mapping, orientation,shaping, indexing, factorization, analysis, rule, template and/or modeloverlay learning. It is to be understood that such learned artifacts maybe later used in such sematic inference when similar semantic contextsare inferred (e.g. shaping and overlay learned models on renderings,graphics, images, frames and/or perform semantic analysis etc.).

User pointed trajectories on a display surface may trigger semanticinference on the semantic network model artifacts that the trajectoryselects, encompasses and/or intersect; further, the inference may spreadto further associated semantic artifacts. The network model artifacts inthe trajectory and further associated semantic artifacts may be selectedand/or activated based on access control (e.g. the user may have accessonly to specific user controls as related to semantic artifacts and/oridentities). In further examples the user draws and/or specifies areasand/or oriented trajectories associated with the display artifacts andtheir associated semantics; in some examples, such semantics may beassociated with indicators and/or factors (e.g. risk, desire, intentionetc.). In further examples the user trajectories may be associatedand/or used to derive goal artifacts; thus, the system infers semanticdrifts, indexing, overlays, routes and/or sub-models based on theoverlaying of the user trajectory to the semantics and/or model mappedand/or representing the display/ed data. Further, the system may displaysuch inferences on the display artifacts mapped on semantic networkmodel artifacts and/or hierarchical structure encompassing the networkmodel artifacts. In some examples, the system redraws and/or overlayssuch information on a display unit. Alternatively, or in addition, thesystem may invalidate the previous information and/or semantic networkartifacts on the display unit controller. It is to be understood thatthe display unit controller may control and/or be incorporated ingraphic processing units, graphic cards, semantic units, CPUs, ASICs,FPGAs, DSPs, tensor units, graph processors and so on.

The system acquire, groups, links, displays, invalidate, query, overlayssemantic artifacts based on context comprising user authentication,semantic profile, wallet and/or access control. Further, the accesscontrol may be used to allow access to such artifacts.

In some examples, the system uses the inputs from I/O including mouse,keyboard and graphics to determine the objects rendered, activated,their semantic identification and/or mapping; further, the systemperforms semantic analysis and learning and overlays the semanticnetwork artifacts on the display screen based on I/O graphic operations.

Overlays may be associated with templates comprising semanticidentities, profiles, hierarchy level, groups, trails, routes,trajectories and/or composable artifacts and further profiles andtemplates comprising such artifacts; the system overlays the semanticartifacts associated with the template semantics in the mapped area,display, control and/or further user interface. In further examples, theoverlays are rendered and/or mapped based on such profiles and/ortemplates.

Overlaying and further semantic analysis may be used to furtherdetermine rendering of semantic artifacts based on inferred semanticsrelated to color, blurring etc. Further, such rendering is based onsemantic profiles (e.g. GREEN, RED may collapse to 30 BROWN based on asemantic profile and/or 40 GREEN based on another semantic profile;GREEN, RED, BLUR may collapse to a GRAY and as such endpoints, regionsare blurred to gray etc.).

In further examples the system uses an additional orientation and/ordrive semantics provided by user (e.g. using similar or different meansvia semantics, semantic routes etc.) together with the initial semantictrajectory in order to create semantic groups, routes and rules.

Narratives may be generated by the system based on semantic analysis.Narratives may be of a general nature, based on a theme, drive semantic,semantic route etc. The system may select areas of narratives, link themand/or assigns actions to such artifacts potentially based on a furthermapping to semantic models. In further examples, the system may usesemantic analysis and mapping to highlight, select, link and/or overlaydisplay artifacts on narrative components.

In further examples, a user may identify semantic group artifacts (e.g.via selecting it on a touch screen; selecting an area and/or trajectorywith artifacts) and further associate semantic artifacts (e.g.semantics, semantic groups, semantic routes, links etc.) associated withthe identified artifacts. In an example, the user selects and/oridentifies a display area comprising a set of semantic artifacts andthen selects a target trajectory and/or area intersecting further areas,endpoints and/or semantic artifacts, thus allowing the system toassociate the semantic artifacts in the selected and/or identified areawith the intersected semantic artifacts. In further examples, the systemmay mark and/or associate the semantic artifacts of the selected and/oridentified area with the semantic artifacts of the targettrajectory/area and/or intersections. Alternatively, or in addition, thesystem may perform semantic analysis between the selected and/oridentified semantic artifacts and those of the target trajectory/areaand/or intersections and further, associate the semantic analysisinference artifacts to either or both of the selected and/or identifiedsemantic artifacts and/or target trajectory/area and/or intersectionsemantic artifacts. In some examples, the system selects an area with aplurality of attributes and/or terms associated with diabetes semanticsand selects a target trajectory/area through endpoints associated withcardiology, arthritis, psychology and other themes artifacts and as suchthe system is able to present inferences related with the effect ofdiabetes on different themes, graphics, controls and/or areas. Infurther examples, the system may use similar techniques to display theimpact of rain to various trajectories on a road infrastructure. It isunderstood that in some cases the impact may be continuously adjustedbased on the continuous inference on the conditions of the selectedand/or identified area semantic artifacts and/or target trajectory/areaand/or intersections area artifacts. In further examples, the system isable to populate/update a group of graphical control element (andpotentially associated labels) and/or semantic groups thereof (e.g. aspart of a target trajectory and/or area) with information (e.g. label,control type, control content, color, font type and/or other assignedand/or inferred attributes) from selected and/or identified semanticgroup artifacts; the populate inference may be based on semanticinference and/or gating between the information associated with thetarget graphical control element (e.g. label, control type, controlcontent, color, font and/or other assigned and/or inferred attributes)and the selected and/or identified semantic artifacts. It is to beunderstood that the system may perform semantic inference based on drivesemantics and/or gating associated with the target trajectory artifactsand/or groups thereof (e.g. labels, graphical controls, content, controltype, groups etc.) and/or selected and/or identified artifacts. Infurther examples, the system performs semantic analysis based onselected and target shape attributes and further render sematic shapesresulted from analysis (e.g. the system has a composition rulespecifying that a selected artifact associated with an elephant and atarget artifact associated with a (one wheel) bicycle should compose,display and/or route when dragged and/or overlaid on top of each otherin a not-allowable icon, smiley face, image, frame, display field, aquestion request and/or other artifacts; such artifacts may beassociated with semantic identities, semantic attributes and/or furthersemantic artifacts inferred, determined and/or challenged by the system,and, further the system may use further semantic analysis on suchcomposable inferences. In some examples, an image associated with aSMILEY (BEAR) FACE semantic identity is stored (e.g. in memory, disk,mesh, device etc.), generated and/or challenged to be retrieved (e.g.from storage, from an inferred and/or preferred semantic flux etc.); the(BEAR) attribute may be optional and/or being more specific for arequested and/or available semantic identity and/or profile. Further,the system may infer, allow and/or generate artifacts (e.g. images,sounds etc.) associated with NICE SMILE based on ratings, profiles,orientation, group resonance and/or further semantic inference.Alternatively, or in addition, the system may allow SMILEY PANDA BEARbut gate SMILEY GRIZZLY BEAR based on semantic artifacts, entropy,divergence, diffusion, drift and/or further rules and/or profiles. Also,if SMILEY artifacts are not available the system may generate, challengeand/or gate (for) artifacts associated with semantic identities with aparticular drift and/or entanglement entropy from SMILEY; further, itmay gate SMILEY antonyms (e.g. GRUMPY) altogether (e.g. based on(configured) entanglement entropy and/or factors). Semantic profiles,factorizations and/or projections may be used to determine SMILEY and/orrelated artifacts; further, the semantic artifacts associated withSMILEY FACES may be stored (e.g. in memory, database, disk, mesh, file,wallet, device, unit etc.) and/or rated based on inferences and/orinputs from users as results of challenges. In further examples, theuser may augment the artifacts and/or compositions when challenged bythe system (e.g. provide semantic attributes, circumstances, rules,guidelines etc.).

The system may not perform augmentation, render and/or display artifactsassociated with high incoherence and/or confusion factors; however, thesystem may perform augmentation, render and/or display artifactsassociated with high incoherence and/or confusion factors whenchallenging the users and/or semantic fluxes for additional informationin order achieve the goal of decaying the confusion and incoherencefactors. The system may perform augmentation, render and/or displayartifacts associated with high coherence and/or low confusion factors.It is to be understood that the system may perform augmentation,rendering, displaying and/or challenging at endpoints associated withhigh augmentation factors (e.g. high visibility, non-peripheralframe/view etc.) or low augmentation factors (low-visibility, peripheralframe/view etc.) based on the factors associated with inferences.

The system is able to select, enable, render and/or update displaylabels, graphics and/or fields based on semantic analysis. In someexamples, such display labels, graphics and fields are associated withsemantic artifacts whether gated or/not. Further, the system is able toperform inference based on the information on the display controls andthe information of any linked semantic fluxes.

In some examples the system populates and/or selects items in thegraphical controls based on information from fluxes based on particularsemantic identities. In an example, the semantic profiles allow thesharing of various levels of semantic identities based on the semanticsof queries/challenges (e.g. BIRCH CLIMBER, 60 LIKE FUCHSIA HAT, 40DISLIKE FUCHSIA HAT etc.) and thus the system is able to map thoseand/or select the relevant artifacts (e.g. match and/or map items in acombobox UI control based on the semantic identities).

In further examples the system gates images, video frames, semanticwaves and/or other artifacts based on semantic identity; alternatively,or in addition the system may replace and/or augment one semanticidentity with another. Further, the system may mask (e.g. overlaysemantic network models, blur, change color etc.) leadership features ofparticular semantic identities at various hierarchical levels (e.g. maskfeatures related with eyes, skin etc.) and thus only the particularsemantic identities are allowed to pass. Analogously the system may maskobjects and/or tags in documents and/or files; as such, the systemanalyses the documents and/or files for semantic identities and mask theleadership features of identities. In addition, the system may transformthe document in a rendering, image and/or frame where the semanticidentities show and/or are tagged as masked as previously explained.

The system may gate the semantic identities and associated semanticartifacts at various levels of the semantic model hierarchy and/orsemantic infrastructure. Such gating may be based on access controlrules and/or semantic analysis.

Synonymy implies in finding synonym semantic artifacts based onfactoring/weighting, comparison to thresholds, semantic routing,semantic orientation, semantic drifts and other semantic analysis.

In an example, the system uses synonymy to perform semantic clusteringand semantic group inference.

In the same way antonymy implies in finding a semantic form for anartifact or collection of artifacts based on antonyms.

While those examples were described in more details is it understoodthat other semantic techniques may be used as described throughout thisapplication and in the specialized literature.

In some situations, the transformation from another language to the mainrepresentation language may resemble the transformation to and/or from“baseband” in a signal processing system. Such transformation can usesemantic analysis including semantic orientation and/or semantic drift.

Semantic processing units can be used to process semantic models.

Semantic processing units can comprise systems on a chip potentiallyusing field programmable logic and configurable computing where theconfiguration of logical gates and processing cores are being configuredbased on semantic determinations, semantic routes, semantic views, viewframes and/or semantic network model.

Semantic units and architectures are in general more safe and securethan a general processing unit due to build access control in the model.Semantic models may be configured by authenticating users via variousauthentication techniques including biometrics, password, mobile devicecodes, location proofing, time proofing and so on.

An important aspect of IOT systems is security; a semantic systemhandles information at a semantic level is much better positioned toasses, detect, isolate, defend and report system intrusions andanomalies.

The IOT systems have higher security and privacy concerns and hencecontrolled information sharing is required. A semantic gate is a way ofcontrolling semantic information sharing and acts as a semantic privacyand dissemination controller based on semantic gating and/or accesscontrol rules for example.

Access control and filtering is used for controlling the interconnectionto other systems and fluxes.

Security is better achieved if a system exposes a reduced number ofattack vectors. Hence, a semantic system might require just a networkcommunication and/or interface e.g. one port, service point, RESTinterface, callback routine or address etc. and all flux services beinghandled at the semantic level.

Semantic circuitry may consist in a plurality of electronic componentswherein each component has at least one semantic input and output (e.g.semantic, semantic flux) wherein the input/s is/are transformed tooutputs via semantic analysis. Further, the components are associatedwith semantic groups based on an inferred composite semantic andpossibly, factors obtained at a stage in the semantic inference. Theinformation is routed to semantic units and/or semantic groups based onsemantic analysis and semantic routing and via semantic gating. Semanticcircuitry may be semantic gate driven and thus it can be referred as ahardware semantic gate.

In some embodiments the system may use optical components such aspolaritons for semantic circuitry.

The semantic flux between various components may be conveyed andcontrolled in a semantic manner in which the information is controlledbased on semantic rules and model as explained in this application; thismay be achieved via a semantic gate.

A semantic wave or signal may form as a waveform modulated at eachelement based on semantic analysis (e.g. composition, time management,access control, gating etc.). In one example, the semantic wave ismodulated based on a semantic inferred at the element and/or semanticwaves received from other sources/inputs. As such, the semantic waverepresents combinatorial semantics which can be further combined whilethe semantic wave passes through elements. As mentioned above thesemantic waves are routed based on semantic routing to other elements orgroups of elements based on its semantic components. Semantic routingmay be managed using semantic gating on fluxes. The semantic waves maybe generated and disseminated in similar ways with semantic conditioningor other semantic techniques as explained in this application. Thesemantic flux and/or waves may use encryption and authentication atleast two elements (e.g. source and destination).

The semantic gate may be controlled based on semantics artifacts. Suchsemantic artifacts may be validated and/or inferred in relation with theauthenticity in a distributed semantic engine manager based on semanticgroups. Distributed identification, validation, encoding/decoding andsemantic wave generation/interpretation may be based on semantic groupsor multiple semantic groups whether hierarchical or not. The semanticgroups may comprise or define the distributed semantic engine and beused in semantic chaining and validation. In some examples, semanticartifacts are used to represent, encode and/or encrypt semantic trails.In one example semantic trails are associated with chains of custody. Achain may be represented or associated with a semantic network modelwith endpoints comprising or being associated with the semanticinformation and the links representing chain relationships. The semanticnetwork of/and distributed ledger may use semantic analysis andinference for authentication, validation, encoding/decoding, encryptionand chain improvement. In some examples semantic wave encoding/decodingis used to generate/interpret, encrypt/decrypt and validate semantictrails. Further, other non-semantic techniques may be used forencryption, encoding and other operations on semantic artifactsincluding semantic trails.

Further, a semantic flux source and/or semantic wave may issue orcomprise at least one semantic in a semantic block chain and theauthenticity is based on a semantic distributed ledger comprising theblock and represented or associated with semantic artifacts (e.g.semantic groups of subjects, devices, blocks etc.).

In some examples a semantic group and/or semantic distributed ledger isformed to encode/decode a semantic wave; in some examples, no singlemember or subgroup of such semantic groups and/or ledgers comprises allthe semantic artifacts to perform such operation, but the operation isperformed collaboratively using any of the semantic analysis,conditioning and collaboration techniques explained in this application.

A semantic wave may also encode the source of the semantic modulation ateach stage.

As mentioned, semantics are associated with factors, waveforms and/orpatterns; composite semantics may be associated with a combination ofthose. They may be associated with waveforms modulated in a specific way(e.g. based on a pattern and/or amplitude and/or frequency and/or pulseand/or phase), potentially based on composition. Analogously withsemantic artifacts, a semantic wave can be simple or composite; asemantic wave may comprise the semantic composition and potentially theidentification of modules in the semantic route and/or trail modulatedinto the wave via any of those techniques or combination thereof.

Semantic waves may modulate the semantic rules in the waveform in orderfor a receiving processing unit to update its rules, routes, conditionand/or infer the modulated semantics. The system performs processingbetween a semantic wave and a semantic based on semantic analysisincluding orientation and drift.

The system may use covariance, correlation and convolution of semanticwaves coupled to semantic analysis. Further, the system performssemantic orientation and semantic drift between the semantics andsemantic routes comprised and/or inferred from the waves.

Semantic waves and/or fluxes may combine based on semantic compositionrouting, semantic rules and semantic gating. They may combine based onsemantic time management. The encoding of the trails and/or route in awaveform may be based on the marked or inferred semantics at the nodes.Semantic waves may be associated with semantic fluxes and routed throughsemantic routes. They may be encrypted and/or authenticated viadistributed semantic inference where the distributed parties aresemantically inferred and/or defined (e.g. based on semantic groups).Alternatively, or in addition, they may be authenticated via semantictrails and routes which may be encoded in the wave itself and the systemchecks the validity or authenticity of a wave and route based onsemantic analysis including orientation. The orientation and drifts maybe assessed based on the encoded data and the internal semantic modeland rules. In some examples, if the semantic drift of semantic analysisand orientation is large the system may not authenticate theinformation.

The semantic artifacts are inferred by direct observations; hence asemantic model developed in a certain environment would have certaincharacteristics of that environment including a semantic model based onthat environment. Additionally, semantic systems can observe semanticfluxes that come from various sources and can update their models basedon these semantic fluxes and trust relationships that have beenconfigured or established. A semantic system will develop based on thesedirect observations or observations of other semantic systems in thesame or different environments. While the semantic systems with similarsemantic coverage capabilities that develop in the same environmentmight have similar semantic model characteristics or signatures,semantic systems that develop in different environments might havedifferent semantic signatures; sometimes those signatures mightcomplement each other. However, in general, the core semantic inferencerules to which the models have been configured will drive thedevelopment of semantic models.

Coherent semantic inference allows a system (and/or semantic group) toreduce superposition via semantic analysis including composition and/orsemantic collapse.

Semantic signatures may be based on semantic groups. Coherent semanticgroups allow coherent semantic inference based on their semanticsignatures at least on group and/or leadership semantic artifacts.

Incoherent semantic groups may exhibit a continuous increase insuperposition.

It is to be understood that the system may assign and adjustcoherence/incoherence indicators, factors and/or goals; further suchindicator and goal artifacts may be associated with a quantum, budgetetc. Incoherent superposition may determine incoherent collapse(collapse due high superposition factors and/or decayed quanta/budgets).

The system may infer coherent and/or incoherent semantic artifacts (e.g.semantic groups, routes etc.) based on coherent and/or incoherentinferences and/or collapse. Such artifacts may be used later forsemantic analysis in a way that the system will prefer coherentartifacts when the superposition is high and/or the budgets are low or,use more incoherent artifacts when the superposition is low and/orbudgets are high.

High incoherency may be related for example with cyber-attacks, channelerrors, jamming and other abnormal or challenging conditions.

In some examples, high incoherency and/or decayed budgets (e.g.potentially due to sensing jamming and/or other attacks) may collapseinto safety drive routes, hierarchical and/or domain level inferences.

A system may learn based on ingestion, fusion and inputs from multiplesemantic units running current, conflicting, trusted, non-trusted and/oropposed semantic models in the same or different environments. As such,the current model may incorporate other signatures while keeping theboundaries of semantic inference through access control rules andfeedback from trusted sources (e.g. users, other trusted systems etc.).The nature of similarity or dissimilarity between models is provided bythe semantic relationships of semantic rules, semantic orientation,semantic groups, semantic leaders, drive semantics, semantic routes, andother semantic artifacts between the two or more models. Sometimes themodels may be grouped in semantic groups with one or more models orgroups running on different semantic units. In an example, the modelsemantic groups may be determined by semantic attributes which specifythe nature of semantic relationships between models and/or groups (e.g.antonym, synonym, not trusted, trusted etc.).

The system may consider the signature of the environment described byother sources when performing inference on direct sensing data. Thesignature of the environment described by those sources may be biasedand the system uses semantic analysis based on the fusion techniquesexplained for semantic fluxes.

The system may infer leader flux/streams from where to refreshparticular semantics, themes and/or categories. Sometimes the systemuses plans where the system defines or determines a theme template basedon semantic factors and the system uses those plans for semanticinference on flux/stream leadership. In an example, the system Aspecifies that it can trust a flux/stream from system B 0.5 on news and0.9 on weather and as such when semantics are received on those themesthe system B ponders (e.g. multiplying, summing, averaging, semanticfactoring etc.) the composition factors with these trust factors. Incases when a semantic wave is transmitted through fluxes/streams thesystem may perform semantic analysis, gating, convolve and/or crosscorrelate the semantic waves for deriving resulting semantic waves.

Further, A may trust flux/streams C on news with 0.7 and as suchcomposes the news from B and C while pondering, convolving and/orcorrelating it based on the trust, other semantic factors and semanticplans.

The pondering and correlation may be based on semantic distributions andspectrograms in intervals of time (e.g. semantic time). In an example, aspectrogram associated to semantics and/or themes, potentially in asemantic flux and/or wave, may be used.

Additionally, or in similar ways, more granular semantics may berefreshed once they expire or before they expire. The semantics may berefreshed individually or as part of a group, category or theme. Furthersemantics may be refreshed as part of a semantic route, goal semanticand/or factor-based inference and/or any other semantic inference.

In an example, the system reassesses the validity of a semantic viewand/or view frame. As such, the system may not expire inferred semanticsbut instead ask for feedback on other fluxes/gates about the candidatesto be expired. If the system is able to receive feedback and refresh thesemantic (e.g. potentially within a budget), the system may not expireit; however, semantic factors may be affected, and further semanticinferences may be required. If the system is unable to receive feedback,it may elect to expire the semantic and perform further inferences basedon the expiration including updates to semantic routes, views, viewframes etc. Further, the system may use semantic factors and semanticbudgets exposed through semantic gates for inference. Alternatively, orin addition to expiration the system may use semantic decaying.

The system may use semantic expiration to infer negations of the expiredsemantic. In an example, once a semantic of SCREEN TOUCHED decays and/orexpire, potentially after an interval of time or semantic interval oftime, the system may infer a semantic of SCREEN NOT TOUCHED until theSCREEN TOUCHED is inferred again. It is to be understood that thenegation semantics may determine and/or be represented using highentanglement entropy and/or conjugate factors. In some examples, thenegation, conjugates and/or entanglement may be represented usingweights, factors and/or modulated signals; when added and/or composed,the weights, factors and/or modulated signals of the negation,conjugates and/or entanglement result in decayed values which mayfurther trigger lower entanglement entropy and/or semantic collapse. Itis to be understood that the weights and/or factors may be representedas values and/or as modulated signals.

The system may associate some intrinsic behaviors with semanticidentities and/or semantic groups. In an example, for (A/THE) SCREEN theintrinsic behavior for particular endpoints, locations and/or profilesis NOT TOUCHED and hence in order to avoid unnecessary inferences thesystem may decay, block/gate, dispose and/or expire intrinsic behaviorsartifacts (e.g. routes) in association with semantic views.

In some examples, the system requests from a stream/flux asemantic/theme with a particular factor and/or budget; if the factor isnot satisfied then the target flux system may perform inference untilthe target is achieved potentially in the requested budget; it is to beunderstood that such inferences and assessments (e.g. projections) maybe performed in a recursive manner in the semantic network. The flux mayconvey related semantics for a requested semantic theme.

If the initial semantic/theme factor is not achievable, potentiallywithin a specified semantic budget, then the target flux system does notperform inference and may send a negative semantic for the request or,alternatively, the budget in which is realizable.

A semantic wave may comprise/modulate/encode a semantic route and/ortrail. Semantic drifts between semantic routes and/or trails may becalculated at each of the elements based on local semantics (e.g. markedor inferred semantics) using any methods described before. Furtherrouting of the wave and/or flux may be based on the drift. In someexamples the drift is used as a semantic indexing factor and the routingand/or budgets based on this factor. In some examples the semanticindexing is applied on a semantic artifact or semantic drift tolerance,threshold or interval and the semantic indexing factor is calculatedbased on the semantic and/or route.

The system relies on increasing noise to detection ratio on varioussemantic fluxes and semantic waves based on semantic analysis.

Natural phenomena are interpreted via sensing and semanticinterpretation.

While detecting a natural phenomenon the semantic system infers oraugments a semantic artifact through various path in the modelrepresentation. For example, while a camera or heat sensor is detectinga bright light, might infer that is either a sun reflection or a lightbulb ‘BRIGHT’, ‘SUN’, BULB′; additional vision or heat sensingobservations might show that the light is attached to a pole ‘POLELIGHT’ which will actually infer that the light comes from a poweredlight bulb. In general, the semantic fusion takes into consideration thefactors associated with the determinations, so if the confidence factorof ‘BULB ON’ is low because/and the ‘SUN BRIGHT’ is high, and/or becausethe determinations is taken during DAY semantic, and/or maybe becausethe ‘POLE LIGHT’ is low then the system infers that the ‘SUN BRIGHT’. Inthe case that the ‘POLE LIGHT’ factor is high because a camera hasdetected the actual bulb feature then the system might infer that ‘LIGHTBULB ON’. In general, semantic flux challenge, inference and additionalfusion elements which might not have taken in considerations due tolower factors may be a good tie breaker in cases of uncertainty (e.g.high confusion factors, superposition, decayed budgets etc.);alternatively, or in addition the system may infer additional cuesand/or change the orientation in rapport with the semantic space and/orobservations (e.g. change the orientation of a device, model overlay,mapping and/or semantic route, use different semantic routes, anchors,conjugate and/or entangled semantics etc.). It is to be understood thatthe system may organize such composite semantics in semantic groups. Inthe example the system learns that the BULB provides LIGHT which can beON or OFF (e.g. via BULB LIGHT, BULB LIGHT ON, BULB LIGHT OFF).Analogously, such inferences of light parameters may determine forexample inferences of sensor attacks (e.g. infer blinding attack by athird party when there is a projected risk of attack and further infersSUDDEN BRIGHT LIGHT, LIGHT OBTURATION COVER VERY HIGH while there are noprojected sources of blinding other than the potential attacker).

A core semantic artifact or rule has very high or absolute weightsand/or factors which never change or decay.

Semantic systems developing under the same core semantic rules or coresignature but in different environments will have highly compatiblesemantic signature complementary models.

Semantic analysis, semantic gating including semantic wave modulationmay be based on state and/or metadata information from various protocolsincluding network protocols (e.g. TCP/IP, 802.11, 5G NR, Bluetooth,TCP/IP, SMTP, HTTP/S, EPC), data exchange protocols etc.

The segmentation of computing platforms is important in obtaining securecomputing systems. The segmentation includes network segmentation, datasegmentation, function segmentation and others. More often, in generalcomputing systems the segmentation functionality is less flexible,however a semantic system could better understand the needs ofsegmentation at various levels and provide more flexible and secureapproaches.

As such a semantic system can create adaptive/ad-hoc networking subnets,can organize data dictionaries and access control (e.g. on data,processing etc.) in such a way that the optimal segmentation isachieved; further it can use processing segmentation based on semanticmodels, flux/gating and semantic analysis. It can also assign computingpower based on the complexity and/or budget associated to a factor,goal, route, inference etc. As an example, if the semantic chain whichneeds to be analyzed for a semantic goal is large in comparison with acurrent semantic view then the semantic system may assign/route/requestsresources (e.g. semantic units, semantic fluxes) based on thatassessment and possibly on a semantic budget. Such scenarios andoperations may take in consideration the potential collaboratorsadvertised and/or published semantic capabilities including theirsemantic budgets. Alternatively, or in addition, it can request that aparticular semantic inference be computed in a certain semantic budgetand pass that information to a resource hypervisor and/or semantic unitthat may allocate and/or semantic route to the necessary resources inorder to process the data in the required time frame.

The semantic composition includes composing semantics and also gatingand/or expiring semantics based on time, other semantics, factors,access control and others. As such, a semantic expiration mechanism mayhelp with controlling parameters and/or the system resource utilizationincluding memory, processing power, specific processing operations andothers. For communication systems, the control may also includebandwidth and processing related to digital to analog conversion, analogto digital conversion, mixing, filtering, amplifying, up/downconversion, squaring, analog and/or digital signal processing and soforth.

As such the system may eliminate, prune, invalidate, inactivate ordisable the semantics and related semantic artifacts that are linked tolower semantic factors and are not used in semantic routes and semanticcomposition.

The semantic expiration and inactivation/activation control helps withefficiency by releasing and optimizing resources; semantics related withsystem resources and/or the semantics related to computationalrequirements, operation, and/or processing might determine to choose adifferent semantic route over the other for an operation or task; if aninferred semantic or the semantic route is linked to semanticrules/gates (e.g. access control, semantic gate) then the system mayguide the task or operation to a particular unit based on therules/gates; such routing and gating may take in consideration thepotential collaborators' advertised and/or published semanticcapabilities including their semantic budgets; additionally, oralternately the system may control the allocation of resources based onsimilar principles. It is to be understood that the system may use aplurality of semantic routes and/or fluxes at any given time; the systemmay choose semantic routes and/or fluxes with various semantic spreads(e.g. based on shift, drift, diffusion, entanglement and/or entropy) inrapport to goals and/or projections. A semantic system may be configuredas static or more dynamic. In a more dynamic environment, the system mayadapt the semantic routes. In more static systems the semantic routesclosely resemble semantic trails and as such the system has a morepredictable outcome. The predictability of a dynamic system may be alsoachieved by controlling the factors of the semantics and semanticartifacts comprising semantic attributes, semantic groups, semanticroutes, semantic budgets and so on. As explained before, the semanticsystem may use those semantic factors for composition, semantic routeselection, routing and any other semantic analysis technique. Biases maybe used to control the semantic factors of artifacts; in an example, thesystem is instructed to LIKE POTATOES and as such the system will bias(e.g. increase/decrease) the semantic factors for routes that comprisevegetable related artifacts because POTATOES and VEGETABLES areassociated in a semantic group. In further examples, the system may beinstructed to NOT TO LIKE VEGETABLES and as such the system detectssuperposition factors in regard to this instruction and LIKE POTATOES.Since a POTATO may be a part of a VEGETABLES semantic (independent)group then the system may factorize more a likeability indicatorassociated to the route comprising the group member. Alternatively, orin addition, the system may perform projected based inference onquestions and/or routes such as (DO I) LIKE POTATOES (?), (DO I) NOTLIKE VEGETABLES (?) and further infer factors for such routes; furtherit may infer routes such as IN GENERAL DO NOT LIKE VEGETABLES BUT LIKEPOTATOES. Alternatively, or in addition, the system may ask foradditional feedback in order to resolve the superposition.

It is to be understood that while performing inferences the leadershipsemantic artifacts may be inferred and/or specified with particularfactorizations.

The system uses inference based on profiles and/or semantic leadershipin order to control the inference. In an example in a VACATION inferredcontext the system may setup leadership semantic artifacts (e.g.LEISURE, PLEASANT, NO RUSH, 50% LESS POTATOES, 80 EVERY MEAL WITH MEAT)potentially based on semantic profiles. It is to be understood that whenthe leadership semantic artifacts are not met during particular timemanagement (e.g. at MEAL there is no MEAT available and/or is associatedwith a deny/denied/block/blocked semantic artifact) the system maypursue the current meal inference and create a semantic route, timemanagement and/or goal of MEAT—NEXT MEAL; further, the system mayconsider denied/blocked semantics such as based on LACTOSE ALLERGIESwhich would block them from (projected) meal goals. Alternatively, or inaddition, it may factorize the EVERY MEAL WITH MEAT artifact by possiblyincreasing and/or decreasing factors based on the outcome of theexperience associated with MEAL WITH NO MEAT. In case that the timemanagement rule is exclusive (e.g. 100% EVERY MEAL WITH MEAT) the systemmay not pursue the current MEAL drive inference, perform challengesand/or further inferences on alternate trails, routes and/or semanticgroups. As it is observed, the semantic artifact EVERY MEAL WITH MEATcomprises the discriminator EVERY which may be used as a discriminationbias in current and/or further inferences based on the factorizationinferred after such experiences.

Semantic groups of semantic profiles and/or composite semantic profilesare inferred and/or formed by the system. The artifacts stored inprofiles (e.g. rules, routes, trails etc.) may be composed, selected,weighted and/or factorized based on semantic analysis and/or leadership(e.g. of drive, route/trail, group etc.). The system may need to performsuperposition and/or confusion reduction (e.g. due to high superpositionand/or confusion factors in inferences using the fused profiles) andthus may reassess the fusion of such profiles.

The hardware may be optimized for semantic inference. As such thesignals/inputs/data/information are split in various streams (forexample based on semantic gating and send and/or routed to variousprocessing units. As such the system may process inputs on morefluxes/streams and/or chains based on the semantic model, semantic rulesand semantic routes. Along the analysis chain the system executessemantic inference based on the semantic model and rules at each unit;the rules and model may be learned and updated during semantic inferenceor at other semantic times. The learning and updating may be controlledthrough semantic gating.

The semantic processing units may synchronize based on semantic timemanagement semantic signaling inference (e.g. signal, waveform, values,patterns, pulses) and/or semantic waves.

The system may align waves/signals from various sources possible basedon cross correlation, covariance, peak-pattern analysis, semanticanalysis, determine and learn semantic time management rules.Conversely, the system may use semantic time management to align twosignals and use the techniques specified before to perform semanticlearning (e.g. learn semantic routes and rules based on conditioning anddeconditioning). The signal alignment may be determined based onsemantic routes wherein one or more semantic routes are correlated withthe signals and/or between them; further the alignment may be based onsemantic conditioning. The system uses semantic drift and orientation tolearn semantic artifacts and also uses semantic artifacts for signalanalysis and pattern matching.

In a similar way with signal alignment and conditioning, trajectories ofartifacts may be aligned, and semantic rules learned. A trajectory maybe partially segmented (e.g. based on gating, endpoints, routes, links,sub-models, sub-trajectories and/or semantic groups) and further rulesand semantic routes learned. In an example, two trajectories aresynchronized based on leader semantics and associated semantic artifactsand/or factors associated with at least one common/similar drivesemantic (e.g. composite semantic) in the routes and/or oriented linkstracing the trajectories. It is to be understood that the factor may bepositive or negative in value.

The system may infer through semantic analysis indicators such as a ratefactor and/or indicator of growth/decrease/decaying of factors.

In further examples, the trajectory inference and comparison may bebased on semantic analysis or any semantic artifacts associated with thetrajectory. Semantics associated with trajectory endpoints, links,routes, rules can be analyzed and composed in any way. Further, thetrajectory analysis, semantic analysis and composition can occur and beassociated with artifacts at any hierarchy level of the semantic model.

Trajectories and/or orientations may be analyzed based on comparing thesemantic routes determined by the semantics associated with elementsmapped to the semantic network model. Further, two trajectories and/ororientations may be compared based on the semantics associated withlinks mapped between endpoints from the first trajectory and/ororientation to endpoints of the second trajectory and/or orientation.The orientation may be based on semantic composition on particulartrajectories. Alternatively, or in addition, the orientation isassociated with a drive semantic artifact. The mapping of links totrajectory endpoints may be also based on such techniques and/orcorrelated on time management; as such, the links may represent asemantic correlation in time between trajectories and the system performsemantic analysis on the resulted semantic network model to determinethe semantic space-time correlation between trajectories.

In further examples the trajectories may be analyzed based onconditioning/deconditioning of signals based on their waveform mappingto semantic network models.

Sometimes the system creates transient analysis models, views and viewframes for semantic analysis including route and trajectory comparison.

Semantic abstraction and generalization may work until a certainsemantic level is reached (e.g. based on a semantic route, whether anumber of semantics in a route where used, or based on semantic factorsand/or thresholds, potentially accumulated during inference) until asemantic budget is consumed or until a semantic mission or goal isachieved, potentially within a semantic budget. The system may plan fora semantic budget (e.g. time, cost), and perform the semantic estimationbased on generalization on that budget. The generalization/abstractionmay be related with multi-domain and/or hierarchical knowledge transfer.

As explained throughout application the semantic models are hierarchicaland/or composable and may comprise semantic relationships at any levelfor any artifacts whether semantic, endpoints, links or any others.

The semantic network models can be composed and/or coupled. In anexample they may be coupled for achieving goals and/or inferences. Thecomposition may be achieved through semantic gating on any of the linksand/or endpoints. Further, the composition and/or coupling may beachieved at any level of hierarchies. In an example, the semanticnetwork model A layer GAME is coupled with the semantic network model Blayer GAME. In some examples the layer A-GAME has a different hierarchylevel than level GAME of B. In other examples the layers are coupledand/or routed on a semantic factor basis of the hierarchy levels (e.g.1.1, 2.0, LOW, HIGH, 0.4 GAME, 0.9 HAZARD etc.); the hierarchy levelsare coupled based on the assigned semantic factors of semantic artifactsassociated with the levels and the system couples the models based on asemantic factor interval and/or threshold; alternatively, or inaddition, the system uses group leadership for model coupling. In asimilar way, the system may couple any other semantic artifacts used ininference (e.g. endpoints, links, routes, view frames, views,sub-models, hierarchies and any combination thereof). Further, thesystem uses such couplings and mappings to enhance the mapped coverage(e.g. in a frame, image, semantic vision model, microscopy, spectroscopyetc.).

Composable models allow the linking, connection and/or composition ofsemantic artifacts (e.g. endpoints) based on semantic analysis.

In some examples, composition of models encompasses overlaying modelsbased on location and/or other semantic artifacts (e.g. semantics,semantics at endpoints, links, orientation, trajectory etc.). Overlayingand/or composition may be based on trajectory alignments based onsemantic trails and/or routes.

In addition, the system may apply masks based on semantic gating beforecomposing models and semantic artifacts.

In other examples the model coupling is based on projected and what-iftype of inference for achieving particular goals. In such examples thecoupling, linking and composition of semantics artifacts (e.g.endpoints, artifacts at a particular level etc.) is based on semanticgoal inference on the composable artifacts.

In further examples composition of models may entail performing orissuing commands to the elements mapped to the composable or compositemodel.

A certain semantic unit might be assigned a budget to perform semanticanalysis on a semantic until a semantic factor (e.g. weight) achieves alevel (e.g. a threshold); then the semantic or maybe other semanticsinferred based on thresholding may be conveyed further, possible by asemantic gate. In similar ways the system may assess goal achievement orinference. The semantic may be or not conveyed based on the inferredfactor. Parallel computation might be achieved through these techniquesand the results aggregated based on semantic composition and analysis.In an example, if a semantic/computing unit doesn't respond in aparticular time and/or budget the system continues with the semanticinference which doesn't include the unit's potential response orsemantic. Alternatively, if the processing is based on a budget the unitmay send a partial inference or a no-inference response after the budgetis exhausted. Sometimes the system may stop the semantic inferenceand/or update the semantic model and rules at a unit based on a semanticfeedback from the other units, potentially organized as a semanticgroup; alternatively, the system doesn't stop the semantic inference butwaits until the semantic inference is completed (or partially completed)and/or routes the semantic artifacts to the appropriate units based onthe semantic rules and routes. Alternatively, or in addition, entangledsemantic artifacts provide complementary and or additional inferenceroutes. The routing may include or consider any left non-consumedsemantic budgets and/or overspent budget (e.g. borrows budgets fromanother entity in a semantic group it belongs). As such, the routing andprocessing is adaptive based on semantic budgets.

In other examples the system issues challenges to semantic groups forsemantic inference on a budget and performs semantic and routinginference within the semantic groups based on semantic analysis,potentially when the budget lapses.

The system may challenge a first entity, collaborator and/or group abouta second entity, collaborator and/or group and vice-versa. As such, thesystem may infer factors and/or budgets about the first and/or thesecond collaborator and associated semantic artifacts. In some examplesthe system may infer that at least the first and/or second collaboratoris compromised and thus increases the risk factors of such entitypotentially in rapport with inferred compromised indicators and/orartifacts.

The system uses any of the semantic routing techniques describedthroughout the application to perform semantic flux/gate connection.Thus, the system may be highly predictive, adaptive, dynamic, staticand/or semantic biased.

Multiple waveforms possibly sampled/derived/coded/chirped from a singlesignal can be processed using semantic techniques.

Semantic streams or flux are routed to different units and chains;analysis of semantic budget trails may determine new semantic budgetsand new semantic budget routes.

The semantic time management, factorization, budgeting and gating allowthe inference of the system resources and is critical for semantic routeselection.

Semantics may be associated to artifacts in relation to channelestimation, band/width, frequency selection, modulation, signalwaveforms generation and processing.

These semantics may be used for resource and/or budget estimators andfeed into the semantic chain and/or the semantic model.

As explained above, semantic time management plays a critical role in asystem's capacity to adapt and perform in a reliable manner. As such,semantic connect technologies and semantic fusion ensure timely semanticinference for a semantic connected system.

Because semantic inference may be goal and budget dependent it istherefore important to be able to estimate, measure and/or control theinference in a distributed environment where multiple pieces are gluedtogether through semantic means.

In order to select a proper semantic route, estimation and evaluationmay be required. The estimation and evaluation may be based on or resultin semantic goals and/or semantic budgets.

In collaborative semantic systems with quality of service the resourceallocation for semantic inference is prioritized based on the indicatorsand/or required/allowed budget. The quality of service can be specifiedbased on indicators and/or semantic budgets. Semantic budgets may bebased on time management rules and may be represented, associated orcomprise semantic factors.

The semantic route can be evaluated based on semantic analysis includingsemantic gating with each system performing management of resources or,in the case of distributed inference, routing to the optimalcollaborative systems based on semantics, semantic budgets and othersemantic artifacts.

As an example, when a sub-system receives a request for inference with aspecific budget, the sub-system executes an evaluation of the goal (e.g.based on what-if and/or projected semantic routing and analysis) formeeting the inference (e.g. GIVE ME ALL YELLOW CARS SPEEDING UNTIL NOONor SHOW ME IN THE NEXT 2 MINUTES THE TEN BEST PLACES TO CONCEAL A YELLOWCAR WITHIN TEN MILES OR TEN MINUTES FROM A/THE COFFEE SHOP). As such,the system may be provided with a goal budget (e.g. best places toconceal—IN/FOR THE NEXT TWO MINUTES) and so the system may project basedon the specified and/or inferred budgets; further the goal leadershipbeing CONCEAL with a semantic identity of YELLOW CAR the system may lookfor artifacts which obscure and/or mask the semantic identity of YELLOWCAR. In even further examples, the goal leadership may be hidden and orimplicit based on semantic identity (e.g. BEST YELLOW CARS) and thesystem infers the goal leadership as of being related with factorsassociated with YELLOW CARs wherein the factors are based on semanticinference and the semantic groups and/or routes associated with YELLOW,CAR, YELLOW CAR. In further examples, the system demands and/or askinformation in relation with semantic identities, endpoints and/or areas(e.g. GIVE ME ALL YELLOW CARS WITHIN PARKING LOT A IN THE LAST HOUR) andfurther the system analyses, challenges and/or interrogates theartifacts (e.g. fluxes, sensors) assigned to such areas; it is to beunderstood that such challenges and/or interrogations might triggersemantic inferences based on the challenging semantic artifacts and/oridentities (e.g. such as YELLOW CAR, PARKING LOT A etc.). Further, forfast searching the system identifies YELLOW as a leadership semantic andas such parses the frames for the YELLOW color. Further, the systemparses the frames for CAR semantic and creates the set of frames havingboth semantics and further assessing whether the color YELLOWcorresponds to CAR based on semantic analysis. The system uses semanticanalysis to restrict the artifacts associated with time budgets (e.g.frames WITHIN THE LAST HOUR), semantic identities and/or fluxes (e.g.associated with PARKING and/or more specifically PARKING LOT A).Analogously, the system may require semantic fluxes to GIVE ME ALLYELLOW CARS WITHIN PARKING LOT A IN THE NEXT HOUR OR UNTIL JOHN'SDELOREAN APPEARS and as such the creates a time management and accesscontrol rule which would allow the gate publishing of YELLOW CARsemantic identity and/or associated artifacts (e.g. license plate,semantic scene associated frame, (mapped) semantic artifacts etc.); itis to be understood that the time management and access control rulesare based on semantic identities such as JOHN'S DELOREAN and furtherassessment of the NEXT HOUR semantic in associated with the compositerequest (e.g. using an internal clock inference; and/or using a semanticflux connected clock (e.g. conveying and/or inferred that it can MEASUREHOURS, MINS, SECS) which will be requested for a NEXT HOUR semantic inrapport with the composite request, wherein the clock capabilities maybe determined by sensing and/or semantic analysis).

In other examples, the system learns the goal based on furtherexplanation—e.g. GIVE ME ALL YELLOW CARS BECAUSE I AM LOOKING FOR THEFANCIEST ONE or GIVE ME THE FANCIEST YELLOW CARS—and as such the systemmay gate, sort, display, augment all the YELLOW CARS artifacts based onthe leadership goal of BEING FANCY for more general profiles or BEINGFANCY for <particular semantic profiles>. In the previous exampleSPEEDING might be relative to location mapping and/or semantic profiles;thus, the system and/or observer infers speeding based on semanticanalysis based on such circumstances.

The system may parse video/audio formats and/or frames and performsemantic augmentation. The system analyzes the video/frame/soundcontent, captions and/or descriptions associated with suchvideos/frames/sound and performs semantic analysis and gating thusproviding users with the required frames, video snippets, semanticartifacts and/or semantic groups thereof (whether group dependent and/orgroup independent). In further examples the system is challenged by auser with GIVE ME ALL INSTANCES WHERE JOHN DELOREAN DRIVES A DELOREANand/or GIVE ME ALL INSTANCES WHEN JOHN DELOREAN DRIVES HIS CAR and thesystem analyzes the videos/sound/frames content and artifacts based onthe semantic group, composite semantics and time management rulesassociated with JOHN DELOREAN presence (e.g. as detected by inferringsemantic identification, artifacts and/or routes associated with JOHNDELOREAN, DRIVES, DELOREAN and further JOHN DELOREAN DRIVES (JOHN'SDELOREAN) etc. Further such snippets may contain only the frames and/orartifacts associated with the goal and/or activity (e.g. from whereand/or when the composite semantic is inferred to where and/or whenexpires potentially based on inferred and/or stored time managementrules, semantic groups of activity associated artifacts etc.). When suchsnippets are presented via semantic augmentation they may be extractedfrom the original media artifact (e.g. video, sound format/file) andpresented with inferred captions associated with further semanticaugmentation. Alternatively, or in addition, they may be presentedwithout being extracted from the original media artifact; in someexamples, the identified snippets are marked and/or played in thecontext of the original media artifact. It is to be understood thatfurther challenges from users and/or fluxes, time management rules,indexing, diffusion and/or further semantic analysis may be used tooverlay, gate, adjust, restrict, crop, expand, play, stop, mute, unmute,expire etc. the snippets and/or associated semantic artifacts.

The system may restrict and/or mute (embedded) advertisings and/orartifacts which determine high confusion, incoherency, low resonanceand/or are not allowed (e.g. for particular users, groups, profileetc.).

The system overlays semantic augmentation with briefs related toprojections and/or goals (of user, context, situations, objects, John,semantic identities, groups etc.) and/or further augmentation based onsemantic analysis; the semantic augmentation may proceed in someexamples based on a challenges from the user (e.g. WHY IS JOHN SOSUCCESSFUL, WHAT ARE THE BEST PARTS OF A DELOREAN, HOW JOHN DRIVES ADELOREAN, WHEN AND WHERE I CAN MEET JOHN etc.) and the system uses thesemantic leaderships of semantics of such challenges to perform semanticaugmentation. While the system may infer a bias, drive and/or leadershipfrom the user based on challenges (e.g. the user thinks that JOHN ISVERY SUCCESSFUL). Alternatively, and/or in addition, it may performaugmentation based on semantic analysis and/or profiles exhibitingvarious degrees of drift, divergence, (entanglement) entropy and/orspread from such biases, drives and/or leaderships. In an example, thesystem infers a semantic artifact exhibiting high drift and/or entropybetween (inference on) various semantic profiles and as such the systemperforms semantic augmentation (e.g. by displaying, rendering etc.) ofthe semantic profiles and the associated spread artifacts. Thus, theaugmentation may present various views, layouts and/or overlays.

The system may segment, diffuse and/or display (with particularrendering semantics) the inferred semantic identities and/or semanticgroups. Alternatively, or in addition, it may map and/or overlaysemantic artifacts on such semantic identities and/or semantic groups;semantic profiles may be used for such inferences thus personalizingexperiences based on viewer/s semantic identities. Thus, it is possibleto present semantic augmentation to the user during the semantic timeusing various semantic views based on various semantic profiles. It isto be understood that the system may switch between semantic views basedon the inferred visualizing semantic identity and/or semantic view. Insome examples, multiple views are displayed and overlaid on top of eachother; further, the system may consider and/or use semantic augmentationin regards to entropy, coherency/incoherency and/or confusion factors ofsuch composite semantic views and display/render them based on furtherinferences and/or intervals related with such factors. In general, suchtechniques and/or semantic overlaying may be used for example to suggestand/or analyze team plays in sports games (e.g. hockey, football,soccer, basketball, volleyball etc.), analyze (medical) imaging, maps,routes, object placing etc. Further, such displaying and/or overlayingtechniques may be access controlled and thus only allowed artifacts arerendered (e.g. a team member may have access to all artifacts while a TVshow host may have access only to particular artifacts, levels, views,shapes and/or granularity).

In further examples the system may be challenged on showing (e.g. SHOW)instead of giving (e.g. GIVE); thus the system may use a differentaugmentation method based on circumstances (e.g. SHOW entails renderingon a display while GIVE may entail other modalities such as sound,tactile, wearable feedback, vibration etc.). It is to be understood thatthe challenge may specify a semantic identity (e.g. ME) and as suchsystem may use further associated semantic identity semantic profilesfor augmentation; while specific semantic identities may be provided,alternatively, or in addition, the system may infer semantic identitiesbased on circumstances and/or semantic analysis.

The system may use the gating and/or publishing capabilities to infer onwhich devices and/or semantic groups to allow and/or perform semanticaugmentation; further, such devices and/or semantic groups may beassociated with at least one user, profile and/or semantic groupthereof. Semantic identities and/or semantic groups of devices may beassociated with access control rules which allow the augmentation to beperformed on such devices (and/or semantic groups thereof) if the accesscontrol rule, publishing, capabilities and/or gating allows. In someexamples, in particular circumstances as inferred during semanticanalysis, a device and/or semantic group may be associated with allowingall and/or particular (e.g. based on publishing, budgets, factors,enablement, diffusion etc.) semantic augmentation capabilities whileothers may have the semantic augmentation (and/or content) diffused,blocked and/or gated possibly on particular semantic artifacts. Further,the system may provide gating and/or access control based on inferenceon content (e.g. paragraphs, documents, images, signals, waves etc.).Further content metadata may be used by semantic inference.

The system communicatively couples at least two artifacts such as posts,devices, components, modules, units, fluxes, UI controls, videorenderers and/or further artifacts based on semantic inference and/orrouting. In some examples, such coupling is achieved by establishingad-hoc networking, flux and/or stream connections. In further examples,the system establishes ad-hoc networking/flux/stream connections and/orrouting based on location, endpoint and/or inference that particularartifacts are associated with the same user, profile and/or semanticgroup.

Further, the system may perform implicit leadership and/or routes basedon semantic profiles. In an example, SHOW CARS may determine an implicitroute and/or leadership for YELLOW CARS based on a semantic profile ofthe challenger and/or the challenged.

During challenges and/or semantic analysis the system may performsemantic gating based on location, endpoint, semantics at locationsand/or endpoints.

Semantic gating may be based on semantic analysis and/or semanticprofiles. In some examples the system infers that a CHIEF SUPERVISOR ONDUTY may visualize and/or have access to YELLOW CARS associatedartifacts at a location/endpoint associated with moderately elevatedrisk while SUPERVISOR OFF DUTY may visualize/access such YELLOW CARSonly in high risk or emergency situations (e.g. high risk factors) ornone at all (e.g. because OFF DUTY is negative and/or have high entropyin rapport to ON DUTY).

Based on the evaluation the system may route/re-route the request, maygate the request based on the semantic model and route the parts todifferent sub-systems. In the case of resource-oriented systems, thesub-system may allocate the necessary resources for performing thesemantic inference within the budget. If the sub-system implementssemantic based virtualization (e.g. dynamically allocate resources on avirtualization platform based on semantic inference), then thesub-system may use the evaluation to allocate and/or spawn new virtualresources for the specific semantic artifacts.

The system may use semantic inference to infer semantics for locationsand further perform location-based searching. In some examples thesystem keeps up to date published and/or gated semantics associatedendpoints (e.g. via semantic analysis including time management). Thesystem may infer diffusiveness factors which may be used to index and/ordiffuse semantic artifacts in the semantic field and space. In someexamples of diffusive semantics artifacts, the system assigns and/orfactorizes HAZARDOUS semantics to endpoints based on diffusive (gating)capabilities (of the oriented links between endpoints).

The semantic diffusiveness may be based on diffusion (e.g. atomic,electronic, chemical, molecular, photon, plasma, surface etc.), quantumtunneling and/or gating in the semantic network model and mappedartifacts (e.g. sensors, devices, components, gratings, meshes and/orcrystals). In further examples the diffusiveness may be coupled withsemantic shaping.

Analogously and/or coupled with semantic diffusiveness the system mayperform propagation analysis (e.g. electromagnetic). The propagationanalysis may take in consideration semantic shapes of objects and/orfurther semantic artifacts as mapped and/or detected to semantic space.

In some examples, the system challenges the system (e.g. display, I/O,semantic fluxes, semantic unit, memory, computer etc.) with GIVE ME INTHE NEXT 10 MINS THE HAZARDOUS LOCATIONS THAT I CAN BEAR and thus basedon semantic diffusive analysis and further semantic analysis ofchallenger circumstances and locations performs semantic augmentation;it is to be understood that sub-goals such as BEAR, TOLERATE, ENDURE,ACCEPT, ALLOW may be based on an accepted reward to risk factor inrapport with the composite goal. Analogously, challenges such as GIVE METHE PATH THAT I CAN LIKE results in sub-goals with higher reward to riskfactors. It is to be understood that in some examples the reward may beand/or comprise a risk indicator and thus the reward to risk factorwould be elevated and/or maxed out (e.g. 100%, 0.5V, 3A, verticalpolarization, no quantum spin, 1 etc.).

In an example, the system maintains and manages resources, entitiescapabilities and allocation based on semantics, semantic artifacts andsemantic analysis. In further examples the resource advertises, publishand/or register inferences and capabilities; further, the system mayrepresent and organize resources and capabilities as models, modelartifacts and/or semantic artifacts (e.g. groups, attributes, routes,endpoints, links, sub-models etc.). The system is capable to optimizeresource allocation based on semantic routing and semantic budgets.

The semantic capabilities of a system may be exposed, published andgated via semantic fluxes and semantic gates. As such, a semantic fluxand/or gate may publish semantic capabilities together with validity,decaying times and or semantic budgets for particular semanticcapabilities (e.g. semantic artifacts, goals, factors etc.). In anexample the validity and decaying times are used by a connected systemto assess the routing for inference. In further examples, thecapabilities are inferred based on semantic groupings and semantic modelat various hierarchical levels (e.g. semantic posts group A mapped to anendpoint EA and group B mapped to an endpoint EB form a group C and thegroup C capabilities mapped to an endpoint EC comprising EA and EB areinferred from those of group A and group B). In similar ways semanticbudgets may be used for assessing the optimal routes for inference. Thesemantic gates may refresh this information on a frequency based onsemantic time management associated to particular goals.

The system may perform goal-factor analysis in which the system performsthe inference for achieving particular semantic goals andestablishes/infers the factors and indicators (e.g. rewards) associatedwith achieving the goals or not (e.g. having those factors within aninterval or threshold). The goals may be associated withfactors/ratings/indicators for objects and/or semantic artifacts, forinferring, associating or dissociating particular semantics (e.g.to/from artifacts, objects, entities) or any combination of those.

In some examples the semantic goals may be inferred or specified basedon user inputs. In further examples, the user may specify through aninterface the targeted or allowed factor/indicator for an operation(e.g. risk, cost etc.). and the system performs semantic goal analysisbased on the targeted semantic for the operation and specified factor.

In another example, such as depicted in FIG. 18 , the user specifies ona graph dashboard and/or (semantic) (enhanced) display optimal locationsor trajectory 63 of the goal. In FIG. 17 , the dashed line betweennumbered nodes or endpoints illustrates an actual physical path oftravel. The solid lines between nodes represent semantic links betweennodes, including a link and permitted direction. In examples of FIGS. 17and 18 , the system may map the locations and intersections of thetrajectories on the graph to a semantic network model and performsemantic analysis of the graphs and trajectory at intersection pointscoupled with the semantic routes/trails of the graphs; further, it maybe coupled with semantics and factors specified or inferred based oninputs from a user (e.g. a user specifies the semantic artifact,indicator and/or factor for a divisional link, endpoint, intersectionendpoint, trajectory etc.). Such inputs may consist for example inpointing or dragging a pointing device or finger on a surface, displayand/or touch interface. The semantic analysis may be used to adjust thesemantic model in order to minimize the semantic drift that wasdetermined/inferred based on the feedback.

In some embodiments the dashed lines in FIGS. 17 and 18 may represent,convey and/or be substituted with any representative graphs, charts,plots and/or display elements/components (e.g., statistical, line, bar,candlestick, OHLC, motion, timeline, map, graphs, charts, maps,diagrams, etc.) which may be related to semantic artifacts (e.g.semantics, attributes, indicators, factors, overlays etc.). Further, thesystem may infer and/or map semantic artifacts based on techniques suchas mentioned in this application.

Semantic drifts and factor comparison may be used for assessing goaldrifts; further, the comparison may be associated with a factor of adrift semantic (e.g. semantic capturing semantic differences) that maybe used by the semantic inference as a semantic thresholding comparison.In a particular example rewards or functions of rewards (e.g.accumulation) are used to determine the drift of current inference withdrive and goal semantics including semantic routes; in such an examplethe system may reevaluate the factors (e.g. rewards) within the modelbased on semantic inference. The system sets the goals and performsinference on the goals for determining a set of semantic routes whichare potentially cached, saved and/or activated in memory in associationwith the goal; if the goals are pursued, the semantic engine comparesthe semantic drift between the goal or drive semantic artifacts with thecurrent inferred semantic artifacts (e.g. comprised in a semantic routeor trail). If the drift is exceeding the threshold (e.g. based on afactor value, interval and/or thresholding semantic) then the system mayreadjust the goal or quit the semantic inference while associating aninferred drift to goal factor or indicator to the inferred semanticartifacts, routes, semantic trails and goal (e.g. potentially through asemantic group in which the factor and/or the goal is defining the groupor has leadership status in a group). The system may use indexingfactors associated with semantics in order to perform drift, cost,reward, rating and/or other factors adjustments and/or calculations.

The system may use goals, factors, and indicators rules and/or plans foradjusting and/or indexing goals, factors, indicators and any combinationof those. The factors and indicators plans may be associated withsemantic time management, composition rules, factor rules and otherrules.

Semantic groups of components may pursue common and/or composablegoal-based analysis, wherein the semantic exchanges and routing betweencomponents is performed through semantic fluxes, semantic gates,semantic waves etc.

Those goal based semantic groups may change based on the change of thedrive semantics. As such the semantic groups may change based ongoal-based analysis and/or collaboration.

The system may pursue goals that are inferred and/or received. Thesystem infers goals indicators, goals and drive semantics. In furtherexamples, indicators are specified in the semantic network model viasemantic rules and the system infers the indicators based on semanticinference; in some examples such indicators may be inferred and/orselected and provide optimal inferences. In similar ways the system mayinfer semantics associated with interfaces, sensors, graphs, graphicalcontrol types, dashboards and used for performing semantic augmentation.

The system may pursue goal and/or effect post-inference analysis. Insome examples, the system performs semantic analysis to determine whythe goal has been or not been achieved as budgeted. Thus, the systemuses the recorded semantic trails to perform analysis (e.g. usingwhat-if and/or projected) and infer the semantic artifacts that havebeen the most consequential (leaders) of success/unsuccess orrealization of goal related factors; the analysis may be performed forexample on multiple projections of semantic view and/or frame views andfurther, the system may ask for feedback on projections potentiallyuntil a particular goal (actual and/or projected) has been met. Usingsuch inferences, the system may infer new semantic routes, groups,leaders and artifacts. In some examples, the system creates a semanticroute and/or groups of recommended and/or forbidden semantics and/orartifacts in certain contexts as comprised by the semantic routes,views, groups and other semantic artifacts.

Post-inference analysis may be used with semantic displaying ofinformation. In an example, the system determines the indicators,factors, routes and further semantic artifacts that may have caused thesuccess, failure and/or other indicators/factors/factorizations; in somecases, indicators/factors/factorizations may be specified by users whilealternatively, or in addition, may be selected by the system based onhigh factorizations, goals/sub-goals matching/drift and so forth. Thesystem may mark, group and/or label the display artifacts that areinferred in such a way. In some examples the system groups and/or labelscontrols, dashboards of indicators and/or other user interface artifactsbased on semantic analysis and rendering. In further examples, the userinterface controls are rendered based on semantic artifacts mappingand/or semantic diffusiveness and/or hysteresis. Thus, such renderingsmay facilitate better visualizations and augmentations of projectedfactors, inferences, semantic units control, device control and/orsimulated environments.

In further examples, the system may project the inference of particularsemantics during semantic scene and/or view development (e.g. an objectof a certain semantic group/s behaving in a certain way into the future,future inference of particular semantic artifacts for the object etc.);if the projections are met then the system may further increase thefactorization (e.g. weights, risk, success etc.) of the routes, rulesand semantic artifacts which were used in the projected inference. Ifthe projected inferences are not met then the system may create a newsemantic group/s based on alternative, additional and/or compositesemantics associated with the object (e.g. different and/or moreparticular from the original semantic group/s semantics) and create newsemantic rules, routes and artifacts for the particular semanticgroup/s; further the system may update the semantic artifacts used inthe initial projection to include a factor (e.g. for weighting, risketc.) for the newly inferred semantic group/s and link and/or associatethem with the newly created semantic artifacts.

Alternatively, or in addition, the system may update, factorize and/orinvalidate the original semantic artifacts used in inference (e.g.update the semantic identity, decay etc.). The decay and/or invalidationmay happen for example, if the system is unable to differentiate (e.g.based on drift, goal and/or projected inference) between the semanticidentity of the newly created group and the semantic identity of thesemantic groups used in the initial projections. Alternatively, or inaddition, the system updates the semantic groups (e.g. with the newlyinferred semantics and/or groups) of semantic artifacts used in theoriginal projection and potentially further factorize them; such updatesmay happen if the system is unable to differentiate between the semanticidentities of the semantic groups.

The system uses goal and/or post-inference analysis to adjust semanticmodels and artifacts. For example, at a beginning of a goal-basedinference the system may associate a factorized indicator and/orthreshold to a semantic artifact which may be adjusted and/or changedbased on post-inference analysis. Analogously, the system may adjustand/or associate semantic artifacts to factorized indicators and/orthresholds. In an example, the system has and/or infers a semanticartifact of TYPE X GATE DELOREAN (LIKELY 90% TOO) NARROW; however, afterpursuing the goal of DRIVING CAR THROUGH TYPE X GATE with a factorizeddegree of success it may adjust the initial semantic artifact to TYPE XGATE DELOREAN (LIKELY 10%) NARROW and/or TYPE X GATE DELOREAN NOTNARROW. Further, the system may adjust the semantic groups and furthersemantic artifacts associated with the semantic identities in theinference (e.g. TYPE X GATE and DELOREAN).

When the system seeks multi-goal inference, it may prioritize thesemantic goals via indicators and factors and form pluralities ofsemantic groups and pursuing those in semantic analysis/inference.

The system may accumulate and redistribute factors (e.g. rewards) basedon the pursuing of goals, routes and/or potential feedback. The rewards,feedback, ratings and other factors may be received and inferred fromany data and input including user, sensing entity, internal, externaletc.

Semantic routing of collaborative components/systems/views/viewframes/hierarchies may entail local semantic routing within local model,routing between models and/or routing between components. In someexamples the models and sub-models are coupled based on semantic routingand/or semantic gating.

Semantic fluxes, models and sub-models may be coupled based on semanticanalysis on the gated semantics.

In some embodiments goal and/or mission-based analysis may be used toimplement semantic contracts. In such systems at least two entities arebound by a contract encompassing one or more contract clauses andconditions. Thus, the semantic system defines such clauses andconditions as indicators, goals and/or factors to be achieved andfurther to infer further completion and/or alerting semantics during orafter goal completion. In an example an entity A providing manufacturingfor an entity B is bound by a contract comprising a clause DELIVER EVERYQUARTER 10000 PAIRS OF SHOES FOR SIZES THAT ARE UNDER 100 PAIRS IN THENY WAREHOUSE (e.g. and/or REPLENISH STOCK ONCE THE STOCK IS UNDER 100).Thus the system may infer the UNDER 100 PAIRS IN THE NY WAREHOUSE for anentity type (e.g. SPRINT BLACK SHOES SIZE 10) based on semantic analysis(e.g. inference of HAD INCOMING 10000 SPRINT BLACK SHOES SIZE 10 andinference of EXPEDITED 9900 SPRINT BLACK SHOES SIZE 10) and furtherinfer a composite sematic of UNDER 100 PAIRS and further DELIVER 10000PAIRS FOR the required size (e.g. SPRINT BLACK SHOES SIZE 10) formatching the goals. It is to be understood that the goal inference maybe based on semantic artifacts (e.g. semantic routes, semantic views)whether hierarchical or not (e.g. a semantic route of UNDER 100 PAIRS,DELIVER 10000 PAIRS OF PRODUCT <SHOE product> and/or, potentiallyREPLENISH STOCK <SHOE product> and/or REPLENISH STOCK WAREHOUSE athigher levels) potentially in an access controlled manner.

Thus, the system is able to continuously perform semantic analysis andmatches the initiation and realization of goals whether based onsemantic groups or not. Analogously, the system may consider the routesmatching or comprising particular semantic groups associated withparticular entity instances.

In further examples, the contracting is based on semantic groups and thesystem analyzes the contracts clauses and/or goals based on whether theyare met on a semantic group composite basis.

The contractual clauses and/or goals may be access controlled (e.g.selectively and/or controlled accessible to participant and/or observingsemantic identities) in a potential hierarchical manner. Further, duringthe semantic inference the semantic artifacts and/or semantic views maybe access controlled and thus the semantic inference and augmentationtoward the goals will pursue and/or reveal only allowable routes andfurther semantic artifacts.

The system may further analyze the risk of the contract not being metand adjust the risk indicators and/or factors in connecting semanticfluxes and gates. It is to be understood that multiple risks may beinferred by various entities and groups (e.g. within the semantic groupitself, by the semantic group itself, by the leader, by other semanticgroups etc.) and thus transmitted within the semantic infrastructurewhere further adaptations (e.g. of goals), negotiations, disablement,invalidation, rating, factorization based on semantic analysis andfeedback may be inferred.

The system may infer difficulty factors for the goals and/or furthersemantic artifacts and use them to infer rewards, risks, budgets,indexing and/or further factors. In an example the system infers thatduring winter storms the difficulty of keeping the warehouse stockedaccording with the goals is higher (e.g. the risk of failure is higher)than non-storm days and at hence it may increase the risks (during goaldevelopment), rewards, ratings and/or other factors of the providers inrelation with the achievement of goals. Analogously the user may useindexing on semantic artifacts to further adjust based on suchcircumstances. Analogously, the system may keep track of activities,tasks, projects and/or (associated) goals assigned to various semanticidentities. In an example, the system performs an activity of LEARNABOUT ENGINE SENSOR SUITE in order to achieve the goals of A VERY GOODENGINE MECHANIC, A GOOD (CAR) MECHANIC; it is to be understood that inthe examples the activity and/or goals refers to semantic identitiesand/or semantic routes which may comprise further hierarchical semanticidentities and/or routes—e.g. ENGINE SENSOR SUITE comprises semanticidentities and/or semantic routes such as—SENSOR, SENSOR SUITE, ENGINESENSOR SUITE. Further, the risk factor of not achieving the goal mayentail assessing routes such as LEARN (FROM) BOOKS, LEARN (FROM)COURSES, LEARN HANDS ON etc.; it is to be observed that the route LEARNFROM BOOKS may entail the activity of LEARN in relation with a semanticidentity of BOOKS with a further semantic localization specifier (e.g.specifying artifacts comprised in BOOKS artifacts and/or endpointsassociated with BOOKS) such as FROM which may be inferred based oncircumstances.

The system may perform and/or guide the semantic analysis based on or ofthe loss (e.g. dissociation, un-grouping etc.) of particular semanticartifacts for particular semantic identities. In some examples, thesystem infers and/or projects semantic artifacts, goals, routes, budgetsand intentions based on (composite) loss indicators and/or factors (e.g.risk of loss, cost of loss, reward of loss etc.). Loss factors indicateand/or are associated with positive and/or negative sentiments; positiveand/or negative sentiments can be modeled through loss factors. Thesystem may pursue loss goals, routes, budgets and/or intentions.

Analogously, with the loss semantic analysis the system may perform gainbased semantic analysis based on gaining (associating, grouping etc.) ofparticular semantic artifacts for particular semantic identities. Thesystem infers and/or projects semantic artifacts, goals, routes, budgetsand intentions based on (composite) gain indicators and/or factors (e.g.reward of gain, cost of gain, risk of gain etc.). Gain factors indicateand/or are associated with positive and/or negative sentiments; positiveand/or negative sentiments can be modeled through gain factors. Thesystem may pursue gain goals, routes, budgets and/or intentions. Thesystem may perform inferences and/or projections on factors (e.g. risk,cost etc.) of going over or not meeting the budgets. Further, the systemuses semantic analysis for inferring budgets based on projections, goalsand/or factors.

The semantic budgets may be associated with semantic groups. The budgetsmay be for example inferred within semantic groups and published viagroups leaders and/or gating. Further, only particular semanticidentities and/or groups may have access to particular budgets in aselective way; the system may select one budget over the other based onidentification in the semantic network. Alternatively, or in additions,semantic profiles may be also used for providing access, inferringand/or selecting one budget over the other.

A network component (e.g. network card, ASIC, I/O module, I/O block,digital block, analog block etc.) may be used to analyze the traffic,infer semantics and coordinate transfers based on the semantic model andsemantic rules. The network plug-in may be used for example to infersemantics on the type of data that passes through a link and usesemantic routes and access control rules for transferring it to othersystems and/or components. The network card may comprise a semantic unitand/or include a semantic gate functionality (hardware unit/block orsoftware) in regard to connections to other systems and/or components.

When used with imaging sensors or imaging streams (e.g. video, images)it may map a semantic network model of endpoints and/or links toartifact locations as detected from image and/or frame data. The systemmay map endpoints to near field and far field features and objects; insome examples the mapping is achieved based on perceived depthsemantics, semantic time and/or semantic indexing. It is to beunderstood that the system maps endpoints to particular features,regions, characteristics and/or objects while preserving an overallhierarchical model for the whole semantic field.

The system may perform the mapping on raw data and/or other renderingsof the artifact. In some examples the raw data and/or renderings areaugmented with additional information (e.g. annotations, bounding boxes,labeling, object/region boundaries, segmentation etc.). We will refer toany of the capture raw data, processed captured data and/or renderingsas to rendering data, rendered data, data rendering or similar terms.

Between two times (potentially semantic time) and/or data renderings thesystem may be able to correlate at various points (e.g. endpoints and/orlinks) the semantic models of the data renderings and further to inferthe semantic of shapes modifications, motions and boundaries (seepicture). For example, if the semantic scene is represented by anobserved object, the system maps the semantic model to areas orlocations in a frame/image/capture and/or data rendering of the object.After the system maps a first network model at a first time, and then asecond network model at a second time after the object is rotated forexample, and the system is able to correlate some points between thefirst and the second models, then the system may use semantic inferenceon the two models to derive the conclusion that the object has beenrotated and eventually derive boundaries. The system may use themovements of the detected features, edges and shapes between endpointsfor semantic inference on a semantic network graph. While multiplemodels may be used, a composable or equivalent single modelconfiguration in a mesh and/or hierarchical structure may be used. In anexample, if the renderings are correlated with the presence of lightsources in the scene, then the system may be able to correlate and infersemantics even further based on the light and luminescencecharacteristics found at each endpoint. For example, in such cases, thevisual semantics associated with each point may be coupled to semanticinference within the semantic network models. Sometimes the correlationmay be described based on leaderships semantics which are essentiallysemantic attributes assigned high factors in semantic analysis (e.g. itis assigned a very high factor in semantic composition and is highlydiscriminative against other semantic determinations and/or artifactidentification). Further, the system may use trajectory comparison andsemantic analysis including semantic orientation for semantic inferenceand mapping of shapes, modifications, motions and boundaries (seepicture).

The semantic model might map to a two-dimensional representation in somedata renderings (e.g. images, frames). In addition, the system mayperform near to far field semantic inference and semantic model mapping.In other embodiments where depth detection is available (e.g.electromagnetic scattering/reflection sensors, time of flight camera,depth camera, laser, radio frequency sensors) it captures the depth aswell and couples it with semantic inference.

In general, when referring to location-based endpoints is it to beunderstood that it may refer to a location in a particular contextand/or semantic field. Thus, the location may be related with physicalcoordinates, volumes, regions whether mapped to an environment, artifact(e.g. frame, image, object) and/or potentially with a location insemantic spaces related with sensing, displaying, mapping, rendering,meaning, symbol and/or language representation.

Depth detection helps the system to identity object edges moreefficiently based on the detected difference in depth in the renderingor scene. As presented in this application the depth detection may bebased on arrays of photodetectors that either, expect a reflectiveand/or scattering response based on a transmitted semantic wave orsemantic modulated signal, and/or based on time delay and/or rate ofphotons detection (e.g. between a reset state of the detector andcharging to a particular energy state and/or a threshold of photoncounts and/or energy quanta count).

Semantic models may be updated based on observations from single ormultiple observers.

In some cases, the composed semantic field of multiple observers may notperform exhaustive coverage of a semantic field of a compact area (e.g.represented by an endpoint) or a semantic group.

In some examples the composed semantic field may not be exhaustive (e.g.covering all locations or endpoints) due to masking or obturations ofendpoints in rapport with observers.

In some examples the system determines depth and distance semanticsbetween objects by determining the time difference between when anendpoint semantics change from a particular semantic and/or group toanother. Thus, in an object detection example, if at a first time car Apartially obstructs car B and an endpoint E is mapped in the field ofview to the car A and later to car B maybe because the car A do notobstruct car B anymore at particular endpoint E the system detects thetime semantics of changing conditions and/or semantics at endpoint E (orat the sensing elements associated with endpoint E) and determine depth,distance and potential further semantics based on such time semantics.In some examples the system updates the risk factors of driving throughparticular endpoints and/or groups (e.g. associated with lanes, zones,leader etc.) using the current and/or projected parameters. In someexamples, the risk factors are positively factorized when distanceand/or movement semantics are further factorized (e.g. 60 APPROACHING 80FAST, 80 FAST APPROACHING, GETTING VERY CLOSE, 100% FAST MOVING, MOVINGFAST etc.); analogously risk factors may determine factorization ofdistance and/or movement semantics (e.g. the SLIDE RISK is high then thedistance semantics are factorized and/or indexed accordingly).

In other examples, when the rendering artifacts are provided by asensing entity the semantic inference may be coupled with the semanticsassociated to the movement of a sensor in order to correlate locationsand artifacts in models and further control the sensor based on theinferred semantics.

An endpoint in the semantic network model may be mapped with elements ina sensor (e.g. a photodetector element in a photosensor, an element in arf sensor) and the semantics at an endpoint are inferred based on datafrom the sensor element and attributes associated with the sensorelements. Alternatively, or in addition endpoints may be associated withown semantic network models and/or with semantic groups of elements.

In a semantic network model, the semantics are assigned to artifacts ina graph and the system adjusts the semantic network model based oningested data and semantic inference. The semantic network graphcomprises endpoints and links in a potential hierarchical structure withgraph components representing another semantic network graph. In someembodiments the links are not oriented. Semantic network models allowmanagement of paths, fluxes, routes and semantic inference within thehierarchy. In an example, the system calculates the cost, drifts and/orfactors of the semantic inference based on the levels in the hierarchythat need to be crossed to link or correlate two or more semanticartifacts. Because each hierarchical level may be associated with atleast one semantic artifact, factor and/or indicator the system mayperform semantic composition, semantic factoring, semantic cost/rewardanalysis while traversing the hierarchical structure. The traversal maybe determined or inferred based on semantic routes. The system may usesemantic budget and goal semantic (e.g. semantic, factor, goal/factor)analysis to determine the hierarchies that need to be coupled, composedand/or traversed and additionally may use access control rules todetermine access within the semantic network model (e.g. between thelevels of the hierarchy of the semantic model). In an example, thesystem would not use inference on a level in the hierarchy until certainsemantics or groups are not inferred at a first level of the hierarchyand an access control rule would allow the transition (e.g. viacomposition) at a second level. The transitions may be related withrisks, targets, costs and other semantic factors and goal indicators.Further, the system may use semantic analysis and semantic accesscontrol to determine the coupling and composition of semantic models andsub-models.

The endpoints in the semantic model may be connected via links. Theendpoints and links in the semantic network model may be associated withsemantic artifacts. The semantic network model is adjusted based on thesemantic inference; the adjustment may include the topology coupling,gates, fluxes (e.g. published and/or access controlled), budgets and anyother semantic artifacts associated with the semantic network modelelements.

Sometimes, groups of sensors and/or endpoints are grouped as semanticgroups and the inferences composed in a hierarchical manner.

A semantic network model may be mapped dynamically or relativistic on anobject. In an example the mapping comprises mapping the semantic sensingfield in a more absolute way relative to the detector elements. In anexample for a camera and/or vision sensor the semantic network model maybe mapped statically on the field of view and if the camera consists ofmultiple photo detectors, it may include mapping of photodetectors tothe endpoints and/or links in the model (see FIG. 16 ). Further, themodel can be mapped in a hierarchical way with higher model levelsrepresenting potentially higher-level semantics; further thehigher-level model levels participate in semantic composition only withthe highest semantics in the previous layer/s that are allowed to passbetween layers. The semantic composition may be based and controlledbased on semantic gating and/or access control.

Alternatively, or in addition, hierarchy levels may comprise frames ofat least one sensor semantic network map captured previously; such astructure, with links between endpoints (e.g. sensing elements or groupidentification) within or between levels provides scene developmentinformation. Alternatively, to sensor semantic maps the system maygenerate semantic frame maps for frames captured from cameras, visionsensors and other devices and which are used to map and/or store pixels,groups, locations and/or other features to the endpoints and/or links.

In some examples the system receives semantic scene artifacts (e.g.images/frames) and receives or infers semantics associated with them(e.g. potentially via same or other sources such as voice, text, displaybuttons and interfaces etc.). The system may infer a semanticfactor/drift/shift between its interpretation of the semantic sceneartifacts and the received semantics (e.g. from a description),eventually inferring semantic/groupings factors in relation to therouting and grouping of particular semantics and the source.

In some examples the system detects camera obturation (e.g. lens orcollimator covered by dirt, shadowed, broken etc.) based on frameprocessing and semantic analysis. In such an example the system detectsartifacts, patterns, areas and patches in the frame processing that donot change in time according with semantic analysis or the change is notconclusive; such artifacts create a static pattern and/or dynamicanomalies in the semantic analysis based on a mapped semantic networkmodel and/or do not pass a threshold of certain static and/or dynamicfactors for the semantic analysis. It is to be understood that thesystem may combine static and dynamic factors for assessing suchobturation patterns. In some examples the static artifacts comprisingfactors, mapped endpoints and patterns may be used to assessobturations. In addition, dynamic factors as detected in the semanticnetwork model and movement semantics and factors (e.g. speed,acceleration etc.) may further help inferring anomalies of staticartifacts (e.g. if a post is MOVING then a static pattern and staticsemantic inference on the pattern in the semantic network model mappedto visual/infrared/terahertz image frames may be inferred as anobturation). When the system detects obturation it may mark theobturated area and models accordingly so that the semantic inferencewould consider and eliminate obturations' noise.

In further examples, the movement inference and trajectories of raindrops, wipers (e.g. blades, sprayed cleaners) may be considered in apotential contextual inference (e.g. dirt present etc.). The semanticcoverage or capabilities of a semantic system are related to thecapacity of generating semantic inference based on observations of thesemantic field. Accordingly, patterns can be learned through semanticinference and mapped to various contexts and environments via semanticartifacts. In an example the system learns a pattern comprising at leastone control rule and/or at least one-time management rule and representsit as a semantic route, semantic group or another semantic rule.Semantic routes and semantic rules may be associated with semanticgroups.

The semantic rules may be associated with semantic artifacts such assemantic routes. Therefore, the routing and control aspect is importantin guiding and breaking down the semantic inference. In an example, theaccess control would allow/disallow inferences based on semanticartifacts, rules and/or routes.

In semantic systems various classes of objects may be parts of a samesemantic group and hence sensor data patterns may be related based onthe group. For example, if two cars from different vendors share thesame chassis, and we have data patterns attached to semantics for one ofthe cars during an off-road trip, we can then use related semanticinference artifacts to the second car and be able to infer potentiallywhen that car goes off-road. The suspension can be different and isimportant to correlate the two signals or data by taking inconsideration the characteristics of the suspension (e.g. via signal orsemantic waveform conditioning and/or suspension semantics gating) andas such mapping this data to the semantic model and rules allow theimprovement of semantic inference. Causality may be modeled wherepatterns, artifacts, entities and/or groups influence one another. Insome examples the causality may be modeled as semantic routes,endpoints, links (e.g. oriented) and/or other semantic artifacts.

In an example an oriented link and associated semanticsrepresents/models a causality relationship between endpoint A andendpoint B.

Sensors and sensor devices and other data sources can flush data atpredetermined and/or semantic intervals.

Sensors in general produce large data sets and then transferring it overa communication link or network might pose a challenge with bothcommunication, storage and interpretation.

It is important that the semantic analysis be done as closest to thesensor at possible. Ideally, the sensor should be coupled to a low powerprocessing unit or device which is able to intelligently draw inferencesbefore transmitting it and/or semantic gating it to other devices.

The advantage of semantic systems is that they are able to understandthe meaning, nature, value and importance (e.g. via factors) ofinformation and hence its transfer requirements. As such, a semanticelement/module/unit may store, expire and/or transmit informationselectively and adaptively based on the overall context assessed at thesystem elements, potentially based on distributed intelligence.

The semantic model associates various semantics to various patterns ofmeasurements, inputs, data and/or semantics.

The system may intelligently route and perform semantic inference on thedistributed semantic hierarchy mapped to various devices.

By also using semantic techniques like semantic groupings, semanticrelationships and semantic composition the sensor data patterns can befurther extended.

While a general semantic model may be built to satisfy the requirementof a generalized audience, it might be that the semantic model need tobe adapted to various personalized requirements. As an example, aperson, identity and/or semantic group might associate a IS COLDsemantic to temperature of 50 F while to another the same semantic mightbe associated with 60 F. Thus, personalized semantic models, sub-modelsand analysis are used based on semantic user preferences and profiles.As such, the semantic profile models may be hierarchical in nature wherethe user's semantic profile models are based on views of anotherprofile/role model (e.g. potentially linked based on a semantic group),which in turn may be a view of a more general model and so forth. Thesemantic views may be hierarchical. It is to be understood that thesemantic profiling and views may be based on drive or orientationsemantics associated with a profile at any level. Also, the accessbetween various views and/or profiles is based on semantic gating andaccess control.

Semantic profiles may be associated and/or based on semantic groups.Thus, various profiles and their associated drive semantic artifacts maybe activated based on the inference, identification and/orauthentication of related semantic identities and groups (e.g.potentially in a hierarchical manner).

The system adapts the inference based on current and/or projectedsemantic identities and associated profiles.

The system may use semantic profiles and semantic gating to ingest andorganize information from a variety of sources. In an example, thesystem ingests text data from a source and create and/or associate thesource of data to a semantic profile which is then used during semanticanalysis; further, the system associates inferred semantic artifactsbased on ingested data to source and/or inferred semantic profiles in apotential hierarchical manner. Further, the semantic profile may beassigned or associated to semantic identities and/or user preferences.

The system may learn and/or infer sentiments based on semantic profilesand/or semantic groups. In an example the system infers that JOHN IS AGOOD BASEBALL PLAYER while further may infer that HE or THEY orTEAMMATES—THINK THAT JOHN IS AN AVERAGE BASEBALL PLAYER.

The system uses semantic profiles to adapt inferences based oncircumstances. The system may use and/or factorize a semantic profileand its artifacts based on (inferred) semantic identities and/orsemantic groups. In an example, if the system observes a soccer game itmay factorize a semantic profile and artifacts of REAL FANS, COACHZIDANE'S FRIENDS etc. and further use such factorized profiles andassociated artifacts in semantic analysis.

A smart semantic sensor, device or component may have a way of knowingwhich semantic should report or allow access based on different semanticprofiles, semantic analysis (e.g. semantic time) and/or possibly onauthentication of a user and/or request. Further, semantic devices mayincorporate only particular artifacts, hierarchies and/or levels of amore general semantic model thus allowing them to efficiently inferparticular semantic artifacts (e.g. lower level); it is to be understoodthat such models may be transferred between devices and within thedistributed cloud based on gating, access control, authentication,semantic profiles, device purpose, goals, contract goals/clauses and/orany other techniques as explained in this application.

The semantic wave may coherently collapse only if the unit has thecollapsible model (e.g. the model needed for coherent semanticinference). Semantic groups of devices may have the collapsible model onparticular themes, semantics, semantic routes and semantic profilesmodulated in the semantic wave. For hierarchical models some devices orunits may have access only to particular hierarchical levels (e.g. basedon gating, access control) and as such, the particular unit might have alimited semantic coverage on the semantic wave, potentially lackingdomain transfer, generalization and abstraction capabilities.Differentiation in semantic coverage may be used to perform encryptionfor example, wherein only particular entities may collapse particularinformation or areas of semantic waves.

Alternatively, or in addition, some units may be provided with a gatedor profiled model and/or gate the model and inference based onparticular interest semantics and semantic routes. In a gated model,artifacts associated with the gated semantics, semantic routes andassociated compositions are disabled, invalidated and/or eliminated.

The semantic flux published semantic artifacts may be accessible onlywithin particular semantic groups and/or profiles. In some examples,only specific themes and associated semantic artifacts as specified by auser are shared and/or published with particular semantic groups and soon. It is to be understood that such publishing, control, profiling andsharing may be analyzed, encrypted, unencrypted and/or authenticated ina hierarchical manner based on corresponding credentials.

Semantic inference produces semantic artifacts. Sometimes the semanticartifacts are associated with raw and/or rendering data and/or renderingconstructs. Semantic artifacts may be reduced or composed to semanticsrelated to shapes, features and/or colors; representations (e.g. maps,models) or other artifacts (e.g. visual artifacts, rf noise artifacts)may be used in inference and/or created during inference and so on.Semantics may be inferred based on model, inputs, location, time andother data.

The system infers semantics and recognizes entities by composingmultiple localized semantics, possibly based on semantic factors andapplying threshold comparisons to the result. The composition may stoponce the system reaches a particular threshold, indicator and/or factorsin an inference (e.g. based on a goal). Sometimes the system performssemantic drive and drift inference based on indicators only.

The system may recognize shapes by semantic inference and grouping onthe semantic network model and/or map. As such, the system may groupelements/artifacts based on semantic grouping and/or semantic linking.

In an example the system has three endpoints EA, EB, EC which may beadjacent. The system groups EA and EB because they are each associatedwith a semantic LEFT RNA. Alternatively, or in addition, the systemgroups EA and EC into an ABNORMAL group because EA is associated with asemantic LEFT RNA and EC with the semantic RIGHT RNA and the systemcontains a semantic composition rule associating RNA at LEFT and RIGHTin close proximity with a composite ABNORMAL semantic. Further, at leastone of the endpoints EA, EB, EC or groups thereof may be linked (e.g.using a model link) via a semantic of LEFT or RIGHT to other endpointsED and EF and groups thereof and the system infers groups based onsimilar principles and potentially clustering those artifacts and/orgroups for more optimized memory access. In some examples the groupingand clustering is hierarchical. In the example the groupings (LEFT,(ABNORMAL, EAC)), (NORMAL, EDF)) implies (ABNORMAL, EAC, EDF) or(ABNORMAL, EACDF). While the example doesn't use semantic factors is tobe understood that factors can be used as well. In some examples theendpoint mappings and groupings may be associated to sensing (e.g.element identification, semantic identification, address, location,state etc.); alternatively, or in addition, elements and artifacts inthe scene, image, frames, maps or renderings (e.g. pixels, area,locations, sub-scenes, sub-frames, objects) are mapped and grouped. Alsois to be understood that the symbolic representation is used in thisexample to convey semantic artifacts, semantic models, semantic routes,and other semantic techniques and structures.

The mapping and association of semantics to raw data may allow thesystem to compose/generalize, construct/deconstruct semantic scenes andobservations. As an example, if a person knows that in a downtown areathere is a big mall and a two-lane road then the system is able toreconstruct the observations by combining the semantics and the internalrepresentation (e.g. images, groups, models etc.) of those artifacts.

If the system uses semantic groups of elements to capture informationand perform inference (e.g. associating an object with a semantic groupof elements and/or identifiers), then the stored semantic artifacts maybe reconstructed/projected based on the mapping, localization and/orsemantics of the element/identifiers/groups to the projectedenvironment. The projected environment may be a virtual environment,remote environment, training room, simulated environment, operating roometc.

Once the system loses (e.g. decays) some semantic interpretation of atype of an artifact then the reconstruction mechanism of an observationmay be altered or become even completely unfeasible. If the semanticartifact has been related, replaced, collapsed and/or fused with/withinother semantic artifacts then the reconstruction may take place usingthose semantic artifacts.

The system may replace or fuse semantic artifacts when there are nostrong links or relationships to such artifacts (e.g. strong semanticroutes, factors, view, view frames, fluxes, groups etc.). In an example,a strong semantic link occurs when the semantic is part of a strongsemantic route or a strong semantic trail. The strong factorizedsemantic artifacts are the ones that are highly semantic factorized inabsolute value. As such, a semantic trail may be high negativelysemantic factorized when the experience of the trail execution had ahigh negative sentiment (e.g. the outcome was far off or even oppositefrom an initial goal or expectation; and/or the system learned strongnew routes). In a similar way, a semantic trail may be high positivelyfactorized if the experience had a high positive sentiment (e.g. theoutcome exceeded the initial goal or expectation; and/or the systemlearned a strong semantic route). In general, the negatively semanticfactors are higher in absolute value than the positively semanticfactors for a particular semantic artifact each time when there is aninference on the particular semantic factors and artifact.

Orientation and drift inference between semantic trails and projectionsbased on the semantic trails and further semantic routes, rules and/orgoals may determine further factors, indicators, sentiments (e.g.nostalgia, regret, guilt, fear etc.) and/or intentions. It is tounderstood that the inference of high intention factors and/or cues maydetermine, infer and/or be associated with low entanglement entropyroutes, goals and/or budgets.

Semantic factors may comprise positive or negative values to reflectpositive or negative sentiment indicators, potentially in rapport with aview, route, view frame, group and any other semantic artifact.

Semantic artifacts may not be always represented with the originalresolution of data; instead they are represented using an approximate ofthe original data or shape for the representative sampling, pattern orwaveform. Thus, the system is able to reconstruct semantic artifacts ina more approximate manner by performing semantic inference/analysis onthe semantic artifacts and/or the goals thereof. Objects, observationsand scene interpretation rely on semantic attributes inference. Semanticattributes may be related with characteristics of semantic artifactsand/or detected objects thus providing superior context interpretation.Scene interpretation may comprise factorized estimation. As such,semantic artifacts may be assessed or compared with/within an area ofthe scene and based on comparison the system may continue to interpretthe scene and area until the goals or factors in the assessment of thescene are achieved. As such, while a particular area of the semanticscene may not yield a particular satisfying result, the overall semanticscene may yield a satisfying result and be classified accordingly basedon the semantics associated with the scene.

The signal processing components take in consideration the semanticscene composition. As such, the system may filter multiple sources ofsignals and/or assigns it to particular semantics or objects based onthe scene interpretation and semantic model. In one example, the systemfilters noisy signals from a semantically identified artifact in thesemantic scene (e.g. filter sounds and/or other signals from a birddetected and/or mapped via optical and/or other sensing means) andmapped to an endpoint; thus the system may use the mapping of artifactsand/or signals to endpoints to perform noise reduction based on semanticanalysis. In similar ways the system may filter low factorized signals,semantic scenes, frames and/or sources.

It is to be understood that the system may perform semantic signalconditioning and/or gating based on semantic groups and/or hierarchies.The conditioning signals, routing and/or gating is/are based on themembers of at least one group/sub-group; further, such conditioningand/or gating may be performed on a composite basis, pipeline and/orhierarchical basis. In some examples, the conditioning and/or gatingwaves and/or signals are composed based on the artifacts (e.g. waves,signals, voltages, sub-groups, trajectories etc.) associated with themembers of the semantic groups and/or hierarchies. In further examples,the system uses groups and/or hierarchies of semantic cells and/or unitsas a pipeline for applying conditioning (e.g. based on semantic groups,on at least one member in leadership positions, each member, sub-groupsetc.).

In optical mapping and rendering the system may use differences inappearance between semantic model artifacts and/or semantic groups tointerpret or render the scene. In further examples the system may usegradients between such artifacts mapped to a layer of a semantic networkmodel. In some examples, color gradients of or between semantic groupsof pixels and/or regions are mapped to a semantic network model. Thus,endpoints may be mapped to pixels, sensing elements and/or semanticgroups thereof and oriented links represent the color or shade gradientbetween or detected by such artifacts. Alternatively, or in addition,frame gradient processing may be used prior to mapping the semanticnetwork model to the processed frame. Also, the system mayhierarchically calculate and map gradients. The system may use ahierarchical semantic model of gradients for inference.

In similar ways with color gradient processing the system may use othergradient mapping to semantic network models. Such gradients may includebut are not limited to gradients on curves, shapes and/or mathematicalgraphs potentially in a multiple coordinate system (e.g. representingobject edges, features, evolution of stock indices graphs, velocitygraphs, acceleration graphs, correlation graphs/matrices etc.). Further,in such examples the intersection of gradients in the particularcoordinate systems may determine new endpoints in the semantic networkmodel.

Location plays a role in determining the semantic scene. Endpoints,links, semantic artifacts and/or semantic scenes are mapped to sensorelements or groups and the system performs analysis on the sensorelements grouping, their characteristics and identities. In someexamples semantic analysis allow the inference of only the differencesbetween/within semantic scenes. As such, the system may update themappings, semantic groups, hierarchical levels and others semanticartifacts. The update inference may include only the changes and/orcomprise only affected artifacts.

In optical mapping and rendering the system may use difference inappearance between semantic model artifacts and/or semantic groups tointerpret or render the scene. In further examples the system may usegradient processed frames with semantic mapping. In one example thecolor gradients between regions, areas (e.g. pixels), features, sensingat elements and/or semantic groups of the former are mapped in thesemantic network model as links and/or endpoints. In further examples aprocessed gradient image (e.g. based on convolution and/or filtering)and/or frame is mapped to the semantic network model. It is to beunderstood that such mapping can take place in a recursive and/orhierarchical manner; in some examples the mapping proceeds based onsemantic inference (e.g. achieving a semantic goal and/or anarea/endpoint is semantically covered, the semantic view doesn't producenew semantically relevant artifacts at particular levels and so on).

Further, the semantic models may be mapped to rendering data and/orsemantic scenes and the system performs inference on the semantic modelsmapped on different data rendering sets and/or at different times. Insome examples the system performs inference only on the routes affectedby changes of semantics (e.g. endpoints and/or links changingsemantics); it is to be understood that the system uses hierarchicalassessment of semantic updates and changes.

The system keeps layers of model mapped to specific artifacts, locationsin order to maintain focus (e.g. follow a goal, a semantic route etc.)and/or preserve high level semantic coherence.

In some examples the system expires, disable, or invalidate semanticartifacts. In further examples entire hierarchies or models may beinvalidated. The invalidation may be based on semantic expiration (e.g.expire a hierarchical level or sub-model associated with a semantic orsemantic group).

The system may need to steer/remap the element grid based on thesemantic field interpretation.

The system may remap the elements or groups of elements to endpoints,links, locations, semantic artifacts and/or semantic scenes (e.g. basedon address, grid, location, identification of the elements, semanticartifacts) while preserving the high-level semantic view frames andviews.

The remapping may comprise updating the associations of the addresses,endpoints, links, locations and/or identifications of the grid elementsto the semantic artifacts, locations and/or semantic scenes withinsemantic view frames and/or views.

In the same way as it performs mapping on sensor element grid, areas,zones the system performs mapping on any detected and/or rendered sceneand artifacts (e.g. pixels, areas, zones, sub-scenes, objects etc.).

In further embodiments the system coordinates detection based on currentsemantic network model. In such an example the system may pointdetection resources (e.g. beams of lasers, infrared, radiofrequency) toareas associated with the network semantic model that don't haveassociated semantics and/or the semantics expired and/or they don'tcomply with a goal-based inference. It is to be understood that thenetwork semantic model is continuously updated and refreshed based onthe semantic analysis including semantic expiration.

The semantics/artifacts/signals of a semantic view frame may be mappedand/or stored in a semantic route. The system may hold more than oneview frame and the system compares the view frames in parallel. Thecomparison may be based on semantic orientation, gating, conditioningand other semantic analysis.

The view frames may comprise or be organized as semantic network modelsand the system performs inference on such view frames.

The system may assign a semantic budget to a view frame for reachinggoals, indicators and/or factors; the budget may be updated as theinference develops.

In some examples the system manages the content semantic view framesbased on semantic analysis and inference. Semantics may be gated onsemantic view frames based on semantic access control. In furtherexamples, the system uses semantic analysis on the goals, indicators andbudgets to allow or disallow artifacts in the view frame. In furtherexamples the system disallows some inferred artifacts and/or providerule updates, ask for feedback and/or generate alerts.

The semantic scene interpretation is optimized in context; contexts maybe captured via semantic models, semantic orientation, semanticprojection, semantic artifacts inferred via semantic analysis andcaptured in semantic routes, semantic views, semantic view frames.Various semantic routes may be preferred over other. As such thesemantics of those semantic routes may be assigned higherweights/factors than those that are not preferred. A context may entaila collection of previously inferred semantics, semantic views, semanticview frames and/or semantic trails and as such the semantic system mayassign or adjust the factors/weights for the semantic routes based on afactor assigned to each semantic in a leadership semantic group. Thesemantic system may also adjust the factors of the semantics in theroute, view frame and/or views based on such leadership; in someinstances, the system may perform further factorization in a recursivemanner (e.g. until a goal is achieved). The semantics may haveassociated particular factors for each semantic route within a viewframe and/or view. Further those factors may be also adjusted based ongoals and leadership inference.

The factors assigned to semantics may determine the expiration and/orsemantic decaying; in some examples, the factors may be associated to aquantum/quanta and/or value/series/waveform in order to performinference and/or decay on semantic artifacts; alternatively, a semanticand/or factor may be used as an indexing value to be applied to aquantum/quanta. In some examples the quantum factorization may determinea time quantum. The time quanta may be associated, determine and/orcomprise semantic time management thus the system being able to“measure” or to rapport inference to the “passing” of time. In otherexamples the quanta are an energy quanta or entropy quanta and thesystem “measures” or rapport inference to energy and/or entropy. Themanagement of semantic quanta uses semantic rule management. Inaddition, or alternatively, the quanta is a signal and the systemperforms inference and decay based on a quantum signal, quantum waveletand/or quantum signal data (e.g. amplitude, frequency, phase, envelope,spectral envelope, spectral density, energy, entropy, gradient,spectrograms/scalograms etc.). The system performs the assessment basedon semantic analysis on such data and/or waveforms wherein semantics aremapped to values/signals and/or are mapped via semantic network modelson signal data (e.g. mapping on envelopes, spectrograms, gradientsetc.). The system may use transforms such as Fourier transforms, wavelettransforms, passbands, window functions to perform semantic inference onthe resulting signal artifacts.

A semantic wave collapse may occur when a semantic quantum factorizationoccurs for the wave. Semantic wave collapse may be used for example tomodel quantum phenomena. A semantic wave collapses onto semantics viasemantic gating when there is a semantic view and/or model for semanticanalysis on the semantic wave.

Semantic validity may be related with the validity overall or with thevalidity within an association with a semantic group, semantic route ortrail. In the same way the validity may be related with associationwithin a semantic view frame and/or semantic view.

The semantic time management (e.g. validity) in a semantic view frameand/or view may be also associated with various profiles based on theuser entity of the view frame and/or view.

A semantic memory cache contains semantic artifacts (e.g. semanticroutes) that are selected for semantic view frame and/or semantic view.The semantic memory cache select/maintain relevant semantic artifacts inthe context. The selection and/or refresh may be semantic driven (e.g.inference, time management etc.)

Semantic memory caches may comprise semantic view frames and/or semanticviews which may be organized in a semantic hierarchical structure.

Alternatively, or in addition to caches the system perform marking,enablement, and/or selection of semantic artifacts in the semanticmemory.

Memory capacitive elements and components associated with the semantics(including marked semantics) and/or factors are charged to a particularvoltage and then discharged in time via variable resistance coupling orvaractors based on semantic analysis and time management.

A semantic memory may comprise semantic artifacts and be organized as ahierarchical structure resembling semantic models. The addressability,control, management and transfer may be based on semantics, semanticanalysis, marking and semantic waves.

In some examples the semantic memory may be a DNA storage.

Semantic artifacts may be inferred and/or associated to DNAencoding/decoding, DNA storage, DNA chains and other DNA artifacts.Further, semantic artifacts may be inferred and/or be encoded in/as DNAchains, molecules and/or proteins. In some examples, the semanticartifacts are related with sequences of amino acids and/or sequence of agenes. In further examples, semantic inference (e.g. decaying,expiration) is associated with protein binding, protein lifespan andother protein associated processes. Protein complexes may be associatedwith semantic inference and analysis (e.g. protein semantic groups,formation/disaggregation semantic routes, semantic rules, timemanagement, access control etc.).

Semantic orientation and/or route on the semantic memory cache may provethat matching semantic artifacts are not available or, the associatedcalculated cost is not acceptable or not matching a semantic budget. Thememory cache is reinforced with semantic artifacts which are more likelyto occur within that orientation context and the factors associated withselection indicators of those artifacts and their components areincreased every time when they occur in similar contexts. Factors can beassociated with drive semantic indicators which can used fororientation/routing to particular goals (e.g. based on higher levelindicators). The selection indicators, contexts evaluations and routingmay be evaluated based on absolute or relative semantic drift andorientation to drive semantics of the semantic routes, trails, shapes,views and view frames.

Analogously to semantic memory cache reinforcement the system may usesemantic selection and marking within the semantic memory. As such thesystem may select/deselect and/or activate/deactivate semantic artifactsbased on semantic analysis. In some examples this is based on semanticrouting within the semantic memory.

Drive semantics inferred at higher levels influences inference andorientation at lower levels and vice-versa. This may occur via semanticgating between the hierarchical levels.

A semantic memory block may be associated with a theme or semantic andthus most if not all of the semantic artifacts related to that theme arememorized/cached there.

A semantic memory may be represented as a semantic network graph atlogical and/or physical level (e.g. based on semantic hardwarecomponents) and the system performs routing and transitions includinghierarchy transitions based on semantic analysis.

Semantic artifacts are grouped based on semantic analysis on multidomain contexts. Multi domain contexts comprise semantic analysis basedon data received from multiple heterogenous sources of informationand/or projected to different domains (e.g. sensing, cyber, network,user interface etc.).

Semantic orientation infers pattern of semantic artifacts potentiallyrepresented in a semantic network model. Thus, spatial shapes may beformed (e.g. composed) based on semantic orientation and/or semanticrouting. Alternatively, or in addition, spatial shape patterns maydetermine semantic routes/trails based on the mapping of the points(e.g. locations) in the spatial shape to endpoints in a semantic networkgraph. The mapping may be determined based on inputs from users,location and presence information, sensing, multi-domain data and otherrelevant information.

The system may perform semantic drift analysis of these paths, shapesand patterns. As such, the system is able to represent shapes andtrajectories or perform semantic inference on shapes and trajectories;the system is able to perform semantic comparison (e.g. semantic drift,drive semantic decaying, semantic collapse) of two or more shapes andtrajectories and/or derive semantic sentiments of shapes, trajectories,comparisons and infers further semantics.

In an example, the system may determine the similarity between twoshapes and/or if the two shapes are related. Further the systemdetermines a semantic attribute of a shape and/or identifies a shape. Inanother example the system determines indicators between two shapes(e.g. risk, likelihood, risk to reward, risk to reward likelihood etc.).In a further example the system determines a complexity factor indicatorbetween two shapes.

The shape comparison may comprise semantic orientation.

The semantic group comparison may be based on a drive or referencesemantic or semantic group (e.g. represented as a semantic networkmodel) where all the candidate semantic groups are compared against thedrive semantic artifact based on semantic orientation, leadership and/orsemantic drift. In similar ways with semantic group orientation thesystem may perform semantic route orientation. In some examples thesemantic routes are represented as semantic groups.

The system may perform semantic orientation in/on semantic view framesin rapport with drive semantics, semantic routes and/or semantic shapes.The system may infer drive semantics that are compared with semanticroutes and shapes (e.g. candidates).

In some examples when hierarchical semantic network models and views areused the system may route the semantic inference based on the drivesemantic artifacts in the semantic view frames and on semanticorientation and gating (e.g. hierarchical gating). Such inference maytake place in any embodiments whether the semantic network model ismapped to physical artifacts, virtual artifacts, picture/video frames,locations etc.

The drive semantics may decay during inference. Once the drive semanticis decayed the inference on that drive semantic may stop. In addition,if a conjugate or entangled semantic is associated with the decayeddrive semantic then the drive inference may continue on the conjugate orentangled semantic artifact.

The system may use and/or infer conjugate semantic artifacts (e.g.semantic identities, groups, shapes etc.). Entangled semantic artifactsmay determine an entangled composition artifact. Conjugate semanticartifacts factors of the same/similar/synonym indicators may decaycompletely when composed; also, a conjugate indicator may be inferredand/or used in further inference when one of its conjugates is highly ormaximal negatively factorized. Conjugates may be related with antonyms,negations and/or conjugate variables in various domains (e.g.time-frequency, Doppler-range, position-momentum, voltage-electriccharge, gravitational density—mass etc.).

Alternatively, or in addition, the drive semantic may be replaced by thenext leader semantic artifact in a semantic group associated with thedrive semantic artifact.

For a particular entity the semantics and semantic profiles can beassociated to sensor data and patterns using semantic inference based onlocalization. For example, if we identify via radio frequency or opticalmeans that an object is traveling between various endpoints, we canrecord the patterns of sensor data and associate semantics to linksbetween endpoints. In an example the system is able to identify throughsemantic inference on localization data and a semantic network modelthat a person is climbing a stair, then we can extract various features,patterns and rules from the data reported from accelerometers,gyroscopes and magnetometers and use that data in future uses toidentify or augment a semantic of CLIMBING. Further, the system maycreate semantic groups associated with CLIMBING for the person,endpoint, link and/or drive semantic artifacts. The system can learn andcreate additional semantic rules (e.g. time management rules) based ondetected semantic groups, semantics, semantic intervals and so on. Thesemantic rule learning may comprise templates, models, semanticartifacts and drive semantics (e.g. CLIMBING related). In some examplesonce a rule is inferred the system may ask for feedback from a user orthrough semantic fluxes and adjust the rating and/or factors for suchrules and potentially validate or invalidate it.

A semantic view is a snapshot in a semantic inference process which maybe associated with inferred semantics and semantic model.

The system may decide that various semantic routes and/or shapes are notfeasible at various times; however, if the system decide that a semanticroute or link is feasible it may use the information related to thenodes in the route and possible current semantic view in order toinitiate various actions, commands etc. In an example, direct sensing(e.g. from optical/RF receive/backscatter, camera/optical sensor/visionsensor) or semantic flux data can be used by a vehicle semantic unit todetermine that a group of pedestrians are traversing the street atlocations in front of the car. Based on various telematics,environmental, capabilities parameters and current view the unit issuesactuation to braking, steering, suspension, electric system based onsemantic budgets, semantic fluxes from other participants, semanticanalysis. In an example, an actuation action is based on an accesscontrol rule comprising a semantic time interval with the system keepinga voltage or current value constant or changing based on factors,intervals and/or plans. Once the semantic time interval changes orexpires another control rule may come into effect which may changeand/or modulate the value further. Is it to be understood that thesemantic rules, semantic interval, semantic timing, weighting, rating,factoring, budgeting and any other semantic rules may be combined in anyway and may be specified as a combination of semantic artifacts,factors, quanta, etc. If a semantic route determined or is related withparticular locations, then the system infers various lateral forcesemantics/factors/routes based on the potential trajectory.Alternatively, or in addition, the system may calculate the lateralforces based on the trajectory and activate only those semantic networkmodel artifacts that are feasible and/or safe to follow. Whileperforming those inferences the system takes in consideration the goalsincluding driving goals where the area/locations/width are required tofit the dimensions of the portion of the vehicle requiring access at anygiven time. The vehicle itself and pedestrians may be mapped to aspatial semantic network model and as such the system may perform theguiding based on semantic routing, shaping, semantic model coupling,time management and any other semantic technique. The system maycommunicate and coordinate with a semantic group of vehicles (e.g.within an endpoint) and/or based on semantic orientation.

In some examples the semantic models may be mapped relatively to thelocation of the observer (e.g. car, sensor, person etc.) andgeoreferenced and synchronized based on additional coordinatedetermination (e.g. land-based positioning, satellite positioning,landmark etc.).

In general, a command may involve more than one actuation or sensor andhence the semantic model may encompass these interdependencies insemantic compositions, semantic rules, semantic routes, access controlrules, semantic model, semantic factors and so forth.

In some examples the semantic is the command and the associated factorscomprise and determining actuation values and/or indexing values.Further, the actuation parameters and/or values may be associated withindicators. In other examples the semantic is a composite specifyingroutes of actuation. In an example, the system may infer based onobservations on the semantic field a PEDESTRIAN IN THE ROUTE (orPEDESTRIAN HAZARD) and subsequently selection of a link/trajectory/routethat avoids the hazard potentially coupled with a semantic route of 1.0EMMERGENCY|LEFT TURN|0.2 RISK ROLLOVER (possibly based on an availableoriented link from the current location to the left and/or onunavailability of an oriented link on the right) and further routing toSTEERING ACTUATE LEFT, ADJUST BRAKE ACTUATOR FRONT, SET BRAKE ACTUATORREAR. Further the system uses factoring rules, time management, accesscontrol and the semantic network model to determine the factoringrequired for such commands (e.g. STEERING ACTUATE LEFT +1.1V, ACTUATORFRONT BRAKE −2.2V, BRAKE ACTUATOR REAR 20 PSI or, in case where unitsemantics/indicators are hidden or implicit STEERING ACTUATE LEFT 1.1,ACTUATOR FRONT BRAKE −2.2, BRAKE ACTUATOR REAR 20). The system maymaintain all the available oriented links from the current locations andcontinuously update semantic routes that would allow the car to followsuch oriented links and/or trajectories. In further examples, the systemmay factorize, eliminate and/or block from the models the links that arenot feasible or pose a danger from the current location. In an example,the links that may be associated with a car rollover, possibly becausethey are not feasible for the car's turning abilities at a current speedand conditions, are marked or factorized as high risk, blocked,invalidated and/or eliminated from the model. While the example providedhas been using a BRAKING assessment for achieving the allowed trajectoryis to be understood that the system may have been using alternate oradditional assessments such as ACCELERATE semantic artifacts.

In the previous example the system inferred an EMERGENCY indicator typeof situation that might have used an EMERGENCY orientation routing andor template for handling the situation. In the case that the situationhave not been deemed as HIGH EMERGENCY (e.g. instead of a pedestrian, awooden box have been detected in front of the car), the system may haveused a different route such as 0.2 HAZARD|0.3 LEFT SWERVE I AVOIDROLLOVER (or 0 RISK ROLLOVER), ADJUST BRAKE ACTUATOR FRONT +3, SET BRAKEACTUATOR REAR +3. Alternatively, or in addition the system may provideindexing commands and factors such as INCREASE STEERING LEFT 0.1 EVERY 2ms UNTIL HAZARD GONE, ACTUATOR FRONT BRAKE +3, ACTUATOR REAR BRAKE +3.The INCREASE STEERING LEFT 0.1 EVERY 2 ms UNTIL HAZARD GONE route couldhave been inferred based on a semantic goal inference such as AVOID BOXIF POSSIBLE (e.g. 0 RISK); thus the system infers the goals, drivesemantics and routes as time management rules, potentially projectedinto the future or soon to be determined (e.g. UNTIL HAZARD GONE, UNTILREACH 80 MPH, TO 80 MPH etc.). Thus, the system may generate semanticinference rules for projections based on routes, templates andcontinuously adjust their factors (e.g. based on indicators such asfeasibility, risk etc.). Further the system may store, adjust,invalidate or expire such rules based on the current or projectedfactors and/or goals. In other examples the semantic is a compositespecifying a semantic group and/or chain of actuation (e.g. ADJUSTACTUATOR VALVE OF BRAKES FRONT). In other examples the addressability isnot present explicitly, case in which the system infers the addressbased on the registered, inferred and determined semantics.

A command may be associated with semantic budget rules comprisingvarious actuation and sensor devices. Further, the commands may beassociated with semantic factors, factor rules and plans (e.g. forindexing, linear/non-linear control, progressive/regressive controletc.). Commands may be exercised via semantics and factors.

Semantic commands may comprise semantics, factors and semantic routes,semantic budgets and/or semantic time management associated with those(e.g. PERFORM COVERAGE ANALYSIS UP TO DISK USAGE OF 50%); thus, thesystem infers the factors, budgets and/or limit semantic time management(e.g. DISK USAGE HIGHER THAN 50%) and associate them to the drivesemantics and goals.

In some examples the system may infer drifts, biases or shifts to goals(e.g. applies negative decaying drift to limit semantic DISK USAGEHIGHER THAN 50% in order to maintain original goal of UP TO DISK USAGEOF 50%.

They may be reevaluated based on semantic inference. The semanticbudgets may be composed. As such, when the system infers or transitionsto one semantic based on other semantics then the transition time istaken into consideration in order to calculate the actuation parametersduring the transition duration. Additionally, when a command oractuation occurs, the system may assess or measure its effects andreceive feedback (e.g. through sensing devices, semantic fluxes etc.)thus associating the inferred semantics of the response with semanticartifacts that generated the command; in this way the system may learnand develop its semantic model through action, effect, reaction andlearning.

In an example, based on cause effect, the system performs groupdependent semantic grouping of any cause effect semantic artifacts andobjects including semantics, goals, routes, groups, fluxes, objects,identities, factors etc. The objects may include detected objects orobjects providing feedback through semantic fluxes.

A goal may be achieved or not; when is not achieved the system mayadjust the semantic factors of the semantic drive route in comparisonwith semantics and detected semantic trails in the semantic view andpotentially adjusts and/or form new semantic groups and rules.

In another example, a semantic goal and/or command may be associatedwith a rendering task where the system uses the goal and/or command toplot objects/features on a rendering environment and/or device. As such,a semantic goal and/or command may be specified in terms of PROVIDEINFORMATION with the goal to INFORM USER and the system uses thesemantic model to infer the best semantic route and semantic profile forachieving that command and/or goal which may vary based on semanticviews and/or view frames. In one context, the system may choose asemantic route which is associated with providing the results ofsemantic inference, semantic artifacts and the semantic factors on adisplay and/or dashboard style interface for example. Further the systemmay use semantic models inference, semantic routes and semantic profilesto organize, view and position the information on the display and/ordashboard. In another example, the system may use actuation to controldevices and provide to the user the information that way.

Display or dashboard style interfaces may be generated based on semanticanalysis and inference of semantic artifacts associated with symbolsand/or semantics of symbols (e.g. graphical symbols). In some examples,dashboard and/or controls features may be mapped to a semantic networkmodel and the system renders the semantic network model based on thedisplay controller interface which may comprise a semantic unit. Thesemantic unit performs the rendering or display by issuing commands suchas controlling display units (e.g. pixels) color, illumination, fadingand so forth (e.g. via a voltage, current, optical signal, photon,laser, evanescent wave, polariton etc.).

Alternatively, or in addition, a semantic unit may be used to displaydashboards and controls by ingesting and/or outputting semanticartifacts associated with tags, scripts (e.g. HTML), templates (e.g.XSLT) and/or programming languages.

The system may be able to use one or more semantic units and display inany format based on semantic inference. In an example, a semantic unitis used to render dashboards and/or other user interface controls viadirect 110 and/or display surface control. In addition, it may output,overlay and/or display other surface controls based on any otherprotocols, formats and transformations some of which are explainedwithin this application. It is to be understood that the display surfacecontrol may entail using display/graphics frameworks and/or programminginterfaces, display/graphics drivers control, display/graphics devicescontrol and/or other display/graphics capabilities; display/graphicscapabilities may be related to semantic units, graphical processingunits, display/graphics cards and/or components, field programmablearrays, other display and/or graphics components and any combinationthereof.

The display output may entail overlaying gated semantic networkartifacts on the display surface.

The display, overlay and/or linking of the user interface artifacts maybe based on inferred semantics and/or associated artifacts mapped tolocations and/or areas on the rendering medium (e.g. display, memory,buffer, graphic interface etc.).

The semantic unit rendering semantics are determined via semanticanalysis.

In other examples display areas, user interface controls and/or displaycomponents may be mapped to semantic view frames and/or views and thesystem uses semantic display plans to render those semantic view framesand/or views. The semantic display plans may be possibly based and/orusing semantic artifacts in the view frame/view and, the current goals,indicators and/or budgets associated with such view frames/views.

In further examples, the system maps semantic network artifacts (e.g.endpoints and/or semantic groups) to areas on the screen comprisingdisplay interface controls (e.g. text areas, labels, textboxes,listboxes etc.) and uses semantic fluxes and semantic gating to transferinformation between endpoints (e.g. from a source to a destination) andthus between mapped controls. The system may use semantic timemanagement and semantic analysis including semantic routing to enable oractivate the transfer of information between linked endpoints and toissue commands once the transfer is completed. The commands may be basedon semantics, semantic routes, semantic rules and further semanticanalysis associated with an endpoint mapped to a user interface control(e.g. “COMMIT” link, button, auto-commit field etc.). In one example,the system comprises a semantic trail/route of SERVICE FIELDS TRANSFERCOMPLETED, COMMIT SERVICE REPORT and thus the system may use the COMMITSERVICE REPORT semantic to identify an endpoint mapped to a commitbutton and/or the action to be executed (e.g. virtual click, send event,click, submit, reset, clear etc.). Alternatively, or in addition, thesystem identifies the display controls based on frame location mappingand associates identification based on composition in context and/orroute (e.g. JOHN DOE SERVICE REPORT FORM FOR JOHN UNDOE COMMIT BUTTONand/or ACTOR, JOHN DOE, SERVICE REPORT FORM, FOR JOHN UNDOE, COMMITBUTTON; SERVICE REPORT FORM DEFECT DIAGNOSTIC DESCRIPTION CONTROL and/orSERVICE REPORT FORM, DEFECT DIAGNOSTIC TEXT BOX and/or SERVICE REPORTFORM, DEFECT DIAGNOSTIC, TEXT CONTROL etc.). The system may consider thecontextual semantic identification and/or groupings (e.g. of JOHN DOE,dependent and independent semantic groups of artifacts, categories etc.)and/or semantic access rules and profiles thereof to gate, allow orblock the flow of information, commands, inputs and/or control. Further,the system may generate semantic model artifacts, semantic groups andsemantic routes for the identified display controls and infer thelinking of such artifacts and associated semantic rules; such inferencesmay be overlaid on a display and further validated based on a userfeedback. It is to be understood that the system may use I/O interfaces(e.g. display, touch, mouse, graphic cards, buses, sensors, actuatorsetc.), operating system interfaces, software and/or hardware interfaces,development kits, calls, events, memory, buffers, registers and/orcombination thereof to perform detection, inferences and control.Alternatively, or in addition the system may use images, frames and/orvideos whether captured from a display, on a memory/storage and/orstreamed. In further examples, because the (entanglement) entropy,divergence and/or access control between artifacts associated withsemantic profiles and/or identities such as JOHN DOE and JOHN UNDOE iselevated and/or the diffusion is low the system may infer a disablementstatus and/or gradual (e.g. based on time management,resonance-decoherence operating interval, hysteresis etc.) activationand/or rendering for the COMMIT related artifacts (e.g. selected basedon (low) entropy, divergence, access, drift etc.) in the route and/or atan endpoint.

As mentioned, the system understands the context of operation based onsemantic models. We exemplified that the system is able to infer thesemantic identification in context (e.g. SALES NUMBER field of JOHNSERVICE REPORT form or window as captured from displays).

The system controls the access to various endpoints, areas and userinterface artifacts based on semantic access control.

The system learns semantic trails and routes and further infer andfactorize other semantic trails and routes based on semantic analysis(e.g. the system has a route for JOHN DOE accessing service reports andthus further infer other routes for JOHN DOE related with servicing andrelated artifacts). Further, the system may understand and complementthe identification and actions from context (e.g. automatically asking,suggesting and/or pursuing actions, commits, transfers etc.).

Display controls and/or linking thereof may be associated withsemantics, rules, gating, semantic routes and/or further semanticartifacts. In some examples, such association and/or links may bespecified and/or inferred based on inputs from a user. It is to beunderstood that the linking of display controls may be associated todata sources, display artifacts/components, sensing and semantic groupsthereof; further, the linking may be between at least two displaycontrols and semantic groups thereof.

In some examples, the system has or infer rendered or display objects(e.g. a RED CAR, a MEDICAL CHART etc.) as semantic groups and/orsemantic artifacts; as the system detects for example a pointing deviceand/or touch sensing in an area associated with object's artifacts itmay select the whole semantic groups and suggest semantics based onprojection and goal based semantic inference (e.g. MOVE TO RIGHT, CHANGECOLOR, OVERLAY EKG etc.). Alternatively, or in addition to pointingand/or touch sensing the system may use other modalities foridentification sensing of the rendered or display objects (e.g. RED CARand/or LICENSE PLATE 0945 by voice, electromagnetic sensingidentification etc.).

The system may use access control and/or further rules at a locationand/or endpoint to implement time management automation and/or gateparticular semantic artifacts and/or profiles. In an example, a semanticprofile of NURSE IN CURRENT SHIFT is assigned in a (facility) (display)area associated with MEDICATION WAREHOUSE a semantic route of SELECTMEDICATION, ENTER MOTIVE, (ALLOW DISPENSE), (DISPENSE ALLOWED) however,for a semantic profile of NURSE IN EMERGENCY the MEDICATION WAREHOUSE(area) may be associated with a more general, less restrictive route ofSELECT MEDICATION, (ALLOW DISPENSE), (DISPENSE ALLOWED). It isunderstood that the semantic SELECT MEDICATION, (ENTER) MOTIVE may beassociated with user interface controls and/or fluxes associated and/orinferred for such semantics (e.g. SELECT MEDICATION may be associatedwith a DRUG combo-box (e.g. based on low entropy and/or drift) and/orflux while (ENTER) MOTIVE may be associated with a DISEASE (e.g. basedon a low entropy in rapport with a composite MEDICATION MOTIVE) textfield, combo-box and/or flux). In further examples, the system may denycertain operations in a route (e.g. MOUSE CLICK, MOVE RIGHT TO FIELDAREA may be automated and/or allowed for some profiles while (MOVERIGHT) (TO FIELD AREA) may be denied for some profiles).

In some examples, the display rendering may be partitioned betweenvarious semantic groups and hierarchies and as such particular semanticsand/or rendered objects may have particular zones that need to berendered and/or displayed into. Thus, the system may perform for exampleresizing (e.g. RESIZE SMALLER), zoom in (e.g. ZOOM IN A LITTLE) and/orzoom out by further mapping objects and/or artifacts to larger orsmaller semantic groups and/or higher and/or lower hierarchical levelsbased on semantic factors and/or indexing factors inferred usingsemantic analysis.

In further examples, user interface controls which are associated and/orlinked each to a semantic flux and/or group of semantic fluxes arerendered on a display surface. The user interface controls may displayfor example gated semantics and/or graphics artifacts associated withthe gated semantics. The user interface controls may be arranged in ahierarchical structure with at least one user interface controlcomprising at least one other user interface control (e.g. a fluxdisplay button control comprises another flux display button control, adisplay button flux control comprises another display button fluxcontrol etc.). Thus, the semantic inference, rendering, display andcontrol may diffuse and/or propagate based on the displayed and/orrendered semantic hierarchy, layers and/or overlays (e.g. a compositeflux/control diffuses and/or propagates to composition fluxes/controls,a composition flux/control diffuses and/or propagates to a compositeflux/control and so on).

The hierarchy can be displayed by specifying semantic routes to befollowed when selecting through the stacked user interface and/orgraphics artifacts. It is to be understood that the selection can beachieved by modulating the semantic identities of the stacked artifactsincluding their semantics onto a semantic wave and applying compositionwith the pursued semantic route and/or search.

The semantics may represent commands and parameters and the semanticfactors may be used to proportionally adjust the signal commands andparameters.

The system may infer action semantics based on semantic analysisincluding orientation.

In various situations the system may assess various drive semantics andsemantic routes. The drive semantics and/or semantic routes may beassessed based on their applicability in relation with the current goaland/or projected semantic view and/or view frames. The projectedsemantic view/view frame may be based on what-if and/or speculativeinference and may be coupled with semantic orientation. Theapplicability may be established based on sensing data, ratings,budgets, costs, response time, semantic scene, semantic view, semanticfactors, semantic orientation etc. Usually once a drive semantic and/orsemantic route is chosen as applicable the system may group the semanticroute with the context in which was applied and with the resultingaction, reaction, effect, result and/or view which may be associated orrepresented as semantic artifacts.

The applicability of particular drive semantics and semantic routes maybe assessed based on a semantic drift and semantic orientation betweensemantic artifacts. The drift may be calculated as semantic distancesbetween semantic artifacts (e.g. component semantics, trail and route,semantic groups etc.) wherein the distance takes into account semanticorientation, semantic analysis, semantic timing, location, access and/orother factors.

A semantic orientation distance is calculated based on a semantic driftwhich signify the difference between the drive semantic, semantic goaland/or projected semantics (e.g. projected semantic view) and thesemantics of the semantic view. The goals may be associated withsemantic artifacts (assignable or not assignable to objects), factorsand/or budgets.

Further, the semantic orientation drift is based on overlaying and/orinferring drift model artifacts and sub-models on the trajectories to becompared. In general, when referring to semantic artifacts and semanticanalysis on such artifacts is to be understood that they may beassociated with semantic factors and/or semantic budgets.

Sometimes the system doesn't infer the drift and/or orientation based onlittle known signals/data or low factor semantics; thus, the systemcalculates the drift only based on higher factorization data, semanticsand leadership. The lower factor semantics and/or unknownsignals/data/patterns may be associated with semantics within thehierarchical chain in the semantic view and maybe with the semanticsequencing; as such, the system may create inference rules includingtime management rules associated with the unknown signals/data. Theengine assigns semantics associated with inputs and signal noise whetherdiscrete or analog. Further, when the system encounters thesignal/data/patterns in other conditions it may reinforce, change orlearn semantic rules based on the semantic chain development. The systemmay use semantic rules templates based on semantics, semantic groups,semantic routes, semantic shapes, semantic orientation, semanticfactors, semantic rules and any other semantic artifacts in order togenerate new semantic rules. Further, the system may infer new ruleswithout a previous template. In an example the system uses the semanticnetwork model to infer and learn semantics, groupings and relationshipsbetween them. Further, the system may learn semantic rules and groupingsbased on interactions, inferred semantics, semantic views, view framespotentially associated with goals, drive semantics and/or routes.Additionally, the system learns routes, rules and/or templates based onsemantic orientation in semantic views and view frames when the semanticorientation doesn't match inferred and/or projected semantic routes,view frames and/or views. As such, the system determines semanticroutes, semantic time intervals, groupings and semantic rules based onthe semantics with high semantic drift from semantic trails and routesand further based on high factorization and leadership status.

It is understood that a semantic route may be collapsible to a compositesemantic and/or drive semantic. Further a semantic route may becollapsible to other semantic artifacts (e.g. an endpoint or semanticgroup comprising the semantics in the semantic route. The collapse maybe based on factorization, decaying or leadership of the semantics inthe route or based on the route. Thus, the semantic collapse may be usedby the system for semantic learning wherein new semantic artifacts areformed, in a potential hierarchical, access controlled and/or gatedmanner.

Semantic drift may be associated with factors calculated based onsemantic routing between the drifted semantics and the semantics in asemantic route. Semantic artifacts associated with higher hierarchicallevels, concepts and/or themes are grouped together in the semanticnetwork model. Thus, the routing and the calculation of factors (e.g.cost, risk or other indicators) between such clusters and/or hierarchiesmay allow for semantic drift and orientation inference.

Semantic views/view frames change based on semantic inference. Asemantic view/view frame comprises a plurality of semantic inferredartifacts potentially organized in semantic hierarchical and/orrecursive structures (e.g. semantic network model). Semantic views/viewframes may be organized as, and/or be part of semantic hierarchicalstructures and memory.

I some cases, the semantic inference on the lower levels in a semantichierarchy structure is more dynamic than higher levels. The higherlevels may be associated with more generalized information and/ortransfer knowledge. The access between levels of the hierarchy may becontrolled via access control rules and semantic gates; in addition, thelink between hierarchies may be achieved through semantic flux/stream.

A semantic view changes based on ingested data or stimuli. Additionally,the semantic system may use time management rules to initiate changes tothe semantic view. Further, a semantic view of a higher level in thenetwork semantic model may change based on semantic inference from lowerlevels. The semantic view changes may be associated with tuning,switching, enabling, disabling the sensing elements so that the systemcan use new sensorial data to identify and map the semantic scenes. In acyber system the ingested data may be data being exchanged betweenpoints, metadata detected through deep packet inspection, data relatedto code execution, protocol sniffers and/or connections betweencomponents/systems; further, the data may be based on vulnerabilitiesingestion from various sources. Alternatively, or in addition, the datais ingested from sensors instrumented/embedded into the networkinghardware/software, computing hardware/software or any otherhardware/software entity. Further, graphics may be mapped, ingestedand/or represented in the form of meaning representation (e.g. semanticnetwork graph). The graphics may be mapped to the semantic network modeland/or mesh based on location, features, sensor elements and othertechniques explained throughout the application.

The semantic view at particular hierarchical levels doesn't necessarilychange. For example, if the semantics and/or semantic groups remains thesame at a particular level then the semantic view doesn't change.Semantic views/view frames may comprise multiple views/view frames.

In an automotive application, as a car travels the semantic unitperforms semantic analysis and semantic processing.

The semantic route selection is dependent on the semantic scenes asdetected by the sensors, semantic sensor attributes/capabilities,semantic flux/stream data or any other multi-domain data; as the carmoves, the semantic routes are considered by the system for inferenceand/or action.

The semantic trails and/or routes can be organized in semantic routegroups wherein groups of semantic trails and/or routes are coupled,rated and factorized/weighted together; the sematic route groups may bealso connected via semantic trails and/or routes and so forth. As suchthe depth of the semantic route hierarchy can grow as the semanticsystem evolves. The semantic trails, routes and semantic route groupsare associated or represented with semantic artifacts (e.g. associatedwith semantics) and may be mapped to a semantic network model orsub-model.

Since the semantic routes may be assigned to semantic artifacts (i.e.model semantics, semantic groups etc.) they may be represented asartifacts in a semantic network graph. Alternatively, or in addition,they may be used for routing within the network graph by comparing (e.g.drift) the semantics in the semantic route with the semantic artifactsassociated with the graph elements.

One method of operation for a semantic system is one in which thesemantic system may develop semantic views and/or semantic view framesusing various semantic routes which in turn may trigger composition, andfurther routing. The system uses semantic orientation, semanticprojection and semantic drift analysis to determine and/or infersemantic routes and semantic shapes.

The semantic routes can have semantic factors associated with them; thefactors may be dependent and/or calculated based on context and are usedin selecting the semantic routes to be followed in particularsituations. Further, semantic orientation may be used to select routesbased on a semantic drift in relation with other semantic artifacts,routes and/or trails; further, the system may organize such routes andtrails in semantic groups or select the routes based on semantic groupsinference and/or leadership.

The semantic artifacts including the semantic routes are associated andcan be identified via at least one semantic (e.g. name, semanticconstruct, group semantic etc.).

The semantic factors can be semantic rule and semantic time dependent.Thus, the factors may be based on inferred semantics and/or timemanagement rules which may contain semantic time intervals.

The factors of the semantic routes may decay with time; thus, thesemantic routes can decay; in general, the semantic analysis and rulesapply to semantic routes and their associated semantic artifacts.

In some examples semantic routes can comprise themselves and/or othersimilar and/or related routes in a potential recursive manner. Thesimilar and/or related routes may be based on similarity based onsemantic orientation and semantic drift for example.

The system may use goal-based inference in which it determines thefeasibility of various semantics and semantic routes based on targetedgoals. For example, a post semantic system determines that another postis or will be in its path; the post system performs goal-based inferenceand finds out which are the feasible semantic routes within budgets fromthe current semantic view to the projected semantic view. The system mayfind multiple routes and potentially select them in the semantic memoryor cache. The system may select and/or mark one route over the otherbased on semantic orientation, semantic budgets, costs, rewards or anyother combination of factors. In an example, the semantic enginedetermines that a semantic route exceeds a semantic budget and has highcosts/risk while has little rewards (e.g. based onratings/weights/sentiment/decaying) in the projected semantic view andthus it doesn't pursue the semantic route. Further, the system may notpursue the semantic route because is associated with a deny or blockaccess control rule; the access control rule may be associated with theroute itself and with a semantic artifact in a route. The system mayassess the potential occurrence and timing of the block access controlrule when factorizing (e.g. weighting) or selecting the route. Thesystem selects and deselects the semantic artifacts in memory based onsemantic analysis. The semantic time management and access control isused in the selection/deselection process and influence the semanticrouting within the memory.

The routes may be assessed based on hierarchy where a route at one leveldetermine a route at a lower level and the system may mark, selectand/or bring all those routes or only a selection in the memory/cache.

The system may activate and/or cache semantic artifacts, semantic routesand groups based on endpoint presence, location, semantic models andsemantic orientation. In an example, the system knows that within theCONFERENCE room there is a TV SET, PROJECTOR, PROJECTOR SCREEN,CONFERENCE TABLE etc. and thus it activates such routes and groups.Further, if the system identifies the particular CONFERENCE room andhave a previous semantic model and/or hierarchy for the room which maybe activated/selected/cached, it may know the expected locations andappearances of such components such as TV SET, PROJECTOR, PROJECTORSCREEN, CONFERENCE TABLE and so on based potentially on semanticorientation and further semantic analysis. It is to be understood thatsuch expectations may be corroborated and/or based on sensing at theparticular locations (e.g. by vision, touch, sound, vibration,temperature etc.).

The system recognizes objects based on memory renderings of semanticshapings (e.g. projected, activated, selected etc.). In a furtherexample the system stores for a particular CONFERENCE ROOM or particulartype CONFERENCE ROOM a TV SET comprising a BLACK TRIM, GRAY SCREEN,LUMINESCENCE REFLECTION and as such the system performs a compositememory rendering of the TV SET based on such routes and drive semantics.The memory rendering is composed based on semantic models and it mayfurther be integrated at higher levels with the mapping of the TV SET inthe CONFERENCE ROOM on previously stored location based semantic modelsor templates (e.g. higher-level semantic model and routes; and/ortemplate for CONFERENCE ROOM layout).

As mentioned, in some examples, templates may be stored by the system athigher levels of semantic model hierarchy. In further examples thetemplates are based also on semantic rules, routes and/or semanticgroups; additionally, such artifacts may be modeled in the semanticmodel (e.g. semantic groups may be modeled with endpoints representinggroup elements and links representing the relationship and/or causality;the hierarchy of the semantic groups may also be modeled viahierarchical semantic models).

The system may overlay semantic model templates on active and/orselected semantic models and draw inferences based on semantic analysis.

In an example, the system may infer that a CONFERENCE ROOM is ATYPICALsince it incorporates MONOCHROME DISPLAY. However, the ATYPICALinference might be less strongly factorized if the CONFERENCE ROOM iswithin a HOSPITAL environment and the MONOCHROME DISPLAY is used todisplay X RAY EXAM. Thus, the system may create a semantic route and/orgroup for HOSPITAL, CONFERENCE ROOM, MONOCHROME DISPLAY with theMONOCHROME DISPLAY being less factorized and as such lacking leadershipskills in inferences.

A selection may be based on current inferred semantics in a semanticview or semantic view frame, potentially at scene hierarchical orprofile level. Further, a selection is augmented with semantics in theprojected semantic view and potentially semantics inferred based onsemantic orientation and drift inference between the views. In anexample, a projected semantic view is based on what-if or speculativetype inference. In other examples, a projected semantic view isaugmented with the goal-based semantics.

In some examples the system may use a plurality of projected semanticviews and potentially inferring semantic drifts between them. The systemmay use the semantic drifts for semantic route selection and adjustment;further, the system may use those techniques in comparison with currentsemantic routes, drive semantics, view frames and views.

When the engine selects a semantic route, it may determine semanticbudgets and pursue the semantic development between the current semanticview and the goal or projected semantic view, potentially adjusting thesemantic route and budgets and applying the actions of the semanticinference until a budget is spent.

During the inference towards the goal semantic view it may associate,reinforce and/or decay association grouping between pursued semantictrails, routes, drive semantics and the current semantic view or thedifference between the semantic views (e.g. via semantic orientation,drift, projection, composition). A semantic view itself may beassociated and/or represented via semantic artifacts and the associationwith other semantic artifacts may be represented as other semanticartifacts (e.g. semantic group).

In the previous example, the semantic system may infer a projectedsemantic of “CAR CRASH” involving a car in its path. Further, the systemmay detect the type of the car as being part of a category or part of asemantic group. As such, the semantic model may contain differentavoidance rules based on the type of object or semantic group; furtherin the examples, the system performs goal-based inference with a goal ofreducing impact on the driver side and thus the system applies thesemantic automation in a way that will achieve that goal. In the firstinstance it may infer an approximate semantic route and continuouslyadjust it based on semantic inference, semantic orientation, semanticdrift and semantic factor indexing.

The system may use semantic factors or principles of operation (e.g.high level rules, routes and/or drive semantics) in order to decide thebest semantic routes, locations, paths, actions, actuations etc.; forexample, one of the principle might consist in “not harming pedestrians”and hence in the case that the driving unit projects that is nearlyimpossible to respect that principle by using the current semantic viewcomprising a high level semantic of drive semantic “follow the lane” itmay look at various semantic routes that will respect that principleand/or strategy. If there is no such feasible option then the system mayuse a semantic principle and projection at a higher hierarchical level,for example “minimizing the victims” or “keep the driver safe” andperform the semantic analysis and routing accordingly.

Additionally, in a case of an accident the whole semantic model can bepreserved, and the semantic trails of the unit decisions can be recordedfor further assessment of the happenings, liability etc.

Adaptability is an important aspect of a semantic system. In general, asemantic model enables adaptable systems due to its dynamic learningnature; the semantic model can be refreshed and adapted in real time ornear real time to various conditions.

Sometimes the system may need to maintain the real time status ofsemantics artifacts (e.g. groups of semantics) and as such the systemupdates the factors of those semantics based on time and semanticanalysis. In an example, the system maintains indications for validityof semantics (e.g. SAFE TO DRIVE) and the system may assess the semanticfactor based on sensing and/or inference from semantic fluxes relatedwith weather, road safety etc. In another example the SAFE TO DRIVEindications are associated with a car/truck and/or a group ofcars/trucks and the system maintains indications based on additionalinformation related to ingested data relating to tire condition,consumable condition, servicing needs, schedules, the semantic timemanagement for replacing those parts and others etc. In an example, whenSAFE TO DRIVE decays to a certain level then the system may performvarious actions such impeding the members of the group (e.g. trucks) toleave facility, send alarms, interact with IT and computing systems orany other action.

Sometimes, the semantic system may need to determine semantic groups forachieving a particular mission or operation. The operations and missionsare location, capabilities and time sensitive and as such a semanticinference engine will be very capable on determining the optimumartifacts to pursue the desired outcome. The system may run goal-basedsimulations and projections and the semantic routes may then be used todetail the operational plan including the usage of assets and the mostimportant attributes in various phases of the operation. If theoperation doesn't perform as expected (e.g. predicted semantic driftsand/or budgets from the selected and/or projected semantic routes andviews is large) the semantic system will be able to adapt and computenew operational plan and semantic groups based on the current inputs.

In the cases of autonomous vehicles, it is important that theyefficiently communicate based on semantic groupings of artifacts (e.g.vehicles, features etc.) and as such the semantic system considers thesemantic fluxes activations and/or inputs based on those semanticgroupings which are potentially based on location clustering and/ormapped to a hierarchy in the semantic network model. The semantic fluxcoupling and activation may be based on semantic inference based onsemantic routing which determine the soon to be travelled locationsand/or other semantic factors.

Gated and/or published semantic artifacts may be made available, enabledand/or disabled in an access-controlled manner based on theauthentication of the fluxes and access control profiles. In an example,display controls and/or semantic groups thereof are displayed andcontrolled in such manner.

The simulation may entail inference on target indicators goals andbudgets; a semantic view may be restored to a previous semantic time.Alternatively, or in addition, the system uses semantic orientationbetween a projected semantic view and the current semantic view todetermine drifts and apply those to determine and/or update the currentsemantic view.

Various techniques can be implemented in order to achieve adaptabilityand orientability. Such techniques may include but are not limited toany semantic analysis techniques including semantic shift, drift,orientation, entailment, synonymy, antonymy, hypernymy, hyponymy,meronymy, holonomy. Those techniques are in general associated withsemantic artifacts and semantic models including semantic attributes,semantic groups, semantic routes, semantic rules, endpoints, links andothers.

As explained before, semantic interconnection and semantic modeldistribution enables semantic systems interoperability while extendingsemantic coverage and semantic field interpretation.

Semantic interconnection may consist in semantic fluxes which conveysemantics between entities. For example, in a connected supply chainenvironment, a retail store may be connected to a supplier semantic fluxand ingest a semantic of “SHIPPED VIA GROUND SERVICE” for a particularitem or a category of items; the internal model of the retailer mayinclude a semantic rule that infers a semantic of “WAITING ARRIVAL” forthe item/items which may have been coupled with an action (e.g. issuinga command to an IO controller, electro-optical component, sensor, analogand digital artifact, actuator, raising an alert, issuing an order to asoftware component, service or any combination of those); further, aREPLENISHMENT STATUS may be inferred and a semantic factor preserved toshow that status (e.g. based on an indicator such a risk associated withthe supply chain route and/or other inference routes based on seasonaldemand, item demand, sales etc.). The semantic factor may be adjusted intime (e.g. based on the progression through the supply/semantic chainand/or decaying) and may be associated with a value in a graph, chart,diagram, dashboard or any other graphical interface and/or virtualenvironment; additionally, the “WAITING ARRIVAL” semantic may be coupledwith a budgeting and/or time modeling rule (e.g. time management rule);for example such a rule can specify that the WAITING ARRIVAL has abudget of 100 cost units and/or that the “WAITING ARRIVAL” semantic isvalid for 5 days since has been shipped (e.g. SHIPPED +5 DAYS—whichrepresent the time it takes for the ground service to deliver themerchandise). Alternatively, or in addition, semantic artifacts may haveassociated a risk and/or success indicator that can be potentiallycalculated based on the risk or success of a negative or complementarysemantic such as NON-ARRIVAL or MISSED DELIVERY. In some examples therisk and/or success indicator is based on semantic time managementwherein the risk factor and success factor change based on the semanticsthat are inferred in a semantic view frame; in further examples, thesemantic view frame is associated with factors for goals and/ornegative/complementary goals and performs inference on the factors inthe semantic view frame. Such factors and/or goals may be semantic timebound such as the −2 WAITING ARRIVAL, STOP WAITING ARRIVAL, 10NON-ARRIVAL and/or HIGH PROBABILITY OF NON-ARRIVAL semantic is inferredbased on a circumstance semantic (e.g. DELIVERY AIRPORT BLOCKED) and thesemantic view frame expires due to the timing goals not being achieved.

The internal semantics may be coupled with other internal or semanticfluxes semantics for composite inferences. Alternatively, a semanticfrom a semantic flux may have been directly coupled with an action; ingeneral, a semantic flux semantic is directly coupled to a criticalaction or command only when the level of trust of the external sourceand the semantic determination by that source is high. The level oftrust can be based on various factors including authentication,encryption, sequencing, timing, location, semantics and/or factors. Thelevel of trust is used for example to identify and/or factorizepotential “too good to be true” gated/published semantics, semanticfactors and/or budgets.

In the above example the time modeling represents an important aspect ofsemantic determinations and interoperability. For example, if theitem/items wouldn't have arrived in 5 days after SHIPPED then the systemmay have used semantic composition and expiration to infer for example“MISSED DELIVERY” instead of “RECEIVED” in the case of on time receive.Additionally, a rule could have been in place to send a “NON-DELIVERED”semantic to the supplier for the item/items in question which in turnmay have been used in the internal model of the supplier to infersemantics and take actions. Additionally, the retailer may have beensharing the “MISSED DELIVERY” semantics, groups and indicators to athird party arbitrator, broker or ratings service that could use thesemantic in its internal model to take actions, infer semantics, assignratings and so forth; as such, the semantic flux of the supplier and/orlogistic provider can be rated, weighted or factorized based on semanticdetermination; further, the non-achievement of the goal (e.g. on timeregular delivery, time management inference etc.) may trigger asking,challenging and/or registering the conditions and/or reasons of thenon-achievement (e.g. DELAY BY RECEIVER, FOG etc.).

The supplier and retailer may agree on a semantic model and/or view thatis used for interaction, gating, semantic analysis between their systemsvia semantic fluxes. The semantic model view then can be shared andtransferred between all the stakeholders including the arbitrator,broker, logistic provider, supplier, receiver and such.

The semantic model distribution and fusion can consist in semantic modelreplication, semantic themes model exchange, semantic view and/or viewframe exchange, semantic hierarchy and other techniques andarchitectures. In an example, a particular hierarchy of a semantic modeland/or view is exchanged.

The semantic exchange can involve a private or public infrastructure,cloud and may be based on semantic fluxes and gating etc.

The semantic exchange may be realized also via point to point, point tomultipoint communication or broadcast (e.g. based on semantic groups).The authentication or validations of exchanges may be based on asemantic analysis on semantic groups in the semantic network (e.g. riskand/or semantic factor inference initiated for a semantic group offluxes). In addition, this may be coupled with semantic analysis insemantic trails and semantic routes which may determine grouping and/orrouting between fluxes.

Semantic systems may exchange semantic models, views, themes and such.For example, those exchanges may be required to align the semanticsystems to certain regulations or laws, to allow the synchronization andinteroperability between systems, to enable real time collaboration, toimprove and expand the semantic inference, to expand the semanticcoverage and other circumstances.

In real time environments semantic artifacts exchange may includeexpiration times assigned to the artifacts being exchanged. Also, theartifacts being exchanged may include a priority, cost, rating and/orany other semantic factor which is associated by the transmitting partyin order to inform the receiving party of the semantic field assessmentof the collaborative system. Also, the exchanges may include time modelsor time rules.

For example, in a drone environment, drone A operating in adverseenvironmental conditions may determine that is low on energy and wantsto land in a shared environment. Because it may lack capabilities ofsensing the full environment in those conditions it may be helped bydrone B which just performed a landing and has more sensing and/orsemantic capabilities. As such, drone B may transfer to the drone Asemantic sub-model (e.g. semantic view, semantic view frame athierarchical level with the semantics and related semantic rules,routes, drive semantics and/or operational commands required for a goalof safe landing. However, because the environment may be highlyunpredictable and contested the semantic sub-model may containexpiration times and/or decaying rules (e.g. semantic factor, semantictime etc.) which are inferred by the transmitting or receiving party andrepresent the safe operation for the sematic artifacts and/or goals; thesafe operation may be associated with an indicator for example. Thedrone A it may not use the semantic artifact if its expiration time haspassed or is about to pass. Additionally, the drone B may transmit morethan one semantic sub-model, each having assigned factors/ratings to theassociated semantic artifacts. The receiving party may use thefactors/ratings and decaying in order to assess the best semantic routespotentially based on semantic budgets. Further, drone A might use thereceiving artifacts and plug them in and fusion with its owncapabilities (e.g. semantic network model, semantic rules, semanticroutes). In an example the system uses factors associated with receivedartifacts and integrate them with its own artifacts. In some examples,the fusion based factors may be based on various factor plans thatextend to semantic artifacts.

The operational semantic models, views, semantics and such may beselected and/or cached. When an expiration occurs, the expired semanticartifacts may be deselected and/or pruned. In similar ways, as thesystem manages the semantic artifacts it may also manage the receivedand/or plugged in semantic artifacts.

The semantic system may be on a private or public cloud that may be partor coupled to a brokerage provider or other services.

The semantic exchange service provides visual or other interfaces whichallow the parties to configure the information exchange and may alsodisplay the factors associated with various semantics, semantic fluxes,providers, other semantic artifacts etc. It may use semantic inferenceto suggest various semantic workflows, providers, brokers etc. Asexplained in this application other interfaces may be inferred and/orcoupled with the semantic artifacts and be available on such portals(e.g. UI controls, display controllers, feedback actuatingelements/devices etc.). In some examples these interfaces are selectedbased on semantic inference. In further examples those interfaces may bebased on user selections and/or profiles.

In an example, the system infers a semantic of ENDPOINT6 WARM BLANKETEFFECT which entails a semantic route ENDPOINT6 WARM COLOR FADING IN 2SEC and further of PIXELS ENDPOINTS TO ENDPOINT4 RED 10 GREEN 56 BLUE 99BRIGHTNESS 5 FADING 8 IN 2 SEC which may translate in a semantic routeof GROUP VOLTAGE (OR CURRENT) LED5 LED4 3.8 mV FACTOR 1 and GROUPVOLTAGE (OR CURRENT) LED3 LED2 3 mV FACTOR −2. Thus, such commands maybe applied to any type of display surface and elements (e.g. LED, OLEDetc.) mapped to semantic models.

A user may identify a trusted pool of providers and the system willswitch between them based on semantic factors including ratings, costs,risks etc. Further the system may use semantic analysis for switchingbetween providers (e.g. based on registered capabilities and/or semanticflux/gating).

Once is configured, inferred and/or learned on the semantic exchangecloud the semantic exchange model is transmitted to the parties andtheir semantic models and semantic fluxes configured accordingly.

Any party can charge a fee for providing or allowing semanticinterconnection services. As such, the fees may be charged on particularsemantics, semantic views, semantic fluxes, number of semantic artifactsand any combination thereof. Further, the fees may be based on achievinggoals, factors, drive or leader semantics, budgets and any othersemantic flux/gating and analysis techniques.

Any semantic exchange service or brokerage may adjust the quota for eachprovider based on factors, semantics, semantic factors, decaying and soon.

The scope of a semantic model is to properly and confidently representthe modeled environment in order to infer semantics in an accuratemanner according with the modeled principles.

While big data analytics uses a data lake and large processing of datafor intelligence gathering, a semantic system uses the semantic modelthat is improved over time in order to process real time or just in timedata.

Semantic engines may be used to perform semantic analysis andaugmentation on big data lakes. Thus, the system performs semanticanalysis on the data from the big data lakes. The big data lakes mayinclude databases, files, clouds and any other big data storage andprocessing entity. The system may use timestamps associated with data inthe big data lakes for performing semantic time management analysis.

In further examples, the system uses time-based series of images and/orframe processing for inferring past, current and/or projected views. Insome cases, the image and frame artifacts are associated with inferredsemantic artifacts, grouped and/or further analyzed based on semanticanalysis. The system may sort, ingest and/or output images, framesand/or renderings based on semantic time. In some examples, the systemoverlays semantic augmentation (e.g. semantic models and/or text) onimages and/or frames based on semantic time.

In cases where there is an increased superposition between semantic(frame) view projections and the confusion factor is elevated the systemmay use safety and/or recovery routes and/or fluxes.

In some cases (e.g. the budgets and/or spreads are low etc.), the systemmay factorize more the artifacts related to leaders having mostpopularity (e.g. measured based on the number and/or size of semanticgroups, routes, links and/or further semantic artifacts they belong to).It is to be understood that the system may infer and/or store popularityindicators and/or factors.

The popularity and/or leadership of a particular artifact may increaseas it induces (affirmative) coherency and/or resonance within (related)semantic groups.

The semantic model can be improved through further modeling and/or withthe semantic knowledge that it generates.

The model accuracy is of significant importance in both advancedanalytics for large data sets and real time applications where theexecution of tasks requires accurate just in time decision making.

In one example, a semantic model is generated into a data center intothe cloud and then transferred to the semantic models of other devicescloser to the edge of the network such as gateways, sensors andcontrollers. The semantic model may be selectively transferred to thedevices based on the semantics and semantic rules that are valid at eachgateway or controller. In one example, the semantic model is distributedinto the network between gateways and the gateways select only thesemantic model artifacts or views that are related to their semanticcapabilities. In an example, the gateways may accept only the semanticartifacts or views related to the registered high factorized or markedsemantics (e.g. of their sensors, sub-gateways or managed entities).Sometimes those registered high factorized and marked semantics reflectthe capabilities of semantic groups or a hierarchical semantic topologystructure. As such, the semantic infrastructure reflects thehierarchical, compositional and semantic grouping (clustering) nature ofthe semantic inference and semantic view.

In other examples the system couple semantic sub-models based onsemantics and semantic groups. As such, two subsystems may select and/orexchange endpoints, group of endpoints, and/or sub-models based on theirassociated semantic artifacts. Further, the subsystems may select and/orexchanged sub-models based on semantic identification, semantic marking,semantic orientation and semantic shaping. Alternatively, or inaddition, semantic gating is used for gating semantic model exchanges.

In one example, the system selects or is instructed (e.g. by a user) toselect leader indicators for which the smoothing and biasing indicatorsand/or semantic artifacts in a semantic groups of semantic units and/ormemories should take place. In an example, the system determines valueranges of factors and indicators as goals, semantic intervals and/ordrive semantics.

In an example of defensive behavior and/or driving, the system usesdissatisfaction, concern, stress and/or fear factors associated withzones and/or endpoints in the semantic network model in order to excludezones, endpoints and/or operations. Analogously, in further examples ofoffensive behavior and/or driving the system uses satisfaction,likeability, preference and/or leisure factors associated with zonesand/or endpoints in order to include zones, endpoints and/or operations.

The semantic smoothing may be based on projected inferences in rapportwith defensive and/or offensive behaviors. In some examples the systemmay bias the offensive and/or defensive behaviors based on theassessment of the projected budgets and/or further factors (e.g. risk,reward etc.).

Security is an important aspect of semantic inference. A semantic systemvets the information it receives in order to use it for semanticknowledge generation and semantic fusion.

In order to vet semantic information received via semantic fluxes, asthe information arrives, semantic factors (e.g. weight) are inferredpotentially based on a factor (e.g. risk) associated with the semanticflux. As the fluxes feed semantic artifacts to the system, the semanticfactors adapt and the information from the fluxes is combinedaccordingly.

The system detects objects through signatures, tags, annotations andsemantic analysis thereof.

The semantic inference relies on increasing superposition, conditioningand noise to detection ratio on semantic sensor observations andmeasurements. While focusing on detecting an object's semanticsignatures (e.g. groups of semantic attributes potentially in thecontext), the noise and/or other signatures may interfere with theparticular object signature being sought.

Semantic artifacts representing superposition signals and noise mayaffect and/or become leaders in various fields, locations andenvironments. It is possible that multiple leader artifacts exist. Inorder to detect the original or denoised signal the system performsprojected inference on leaders. For increased recovery of the originalsignal the system may need to infer original signal (e.g. based onsemantic wave) leaders using semantic analysis. The signal leaders varyin time based on the propagation environment. Some environments changeleaders more often than others.

A partial shape or partial signature of an object might be detected inthe semantic field via one or multiple sensors during a semantic fieldcapture however, the presence of the object or signature cannot beinferred unless the leader context within the semantic field capture isunderstood.

Hence, multiple semantic captures and signatures from various sensorsmay be used in order to eliminate noise, determine semantic leaderartifacts and/or augment a particular feature, object, semantic orsemantic group. A semantic (e.g. composite semantic) is a composition intime of features, objects, groups and semantics.

In general, a semantic scene captured in a semantic snapshot of a sensoroperating in highly dynamic environment e.g. a camera/vision sensorinstalled on a fast-moving vehicle is short lived and hence goal andspeculative analysis of the scene development is important. Therefore,the semantic model might incorporate scene development view frames basedon semantic routes and semantic model. The scene development can includebringing semantic artifacts into a cache, assigning a higher selectionindicator (e.g. possible based on semantic factors) determining leadersand drive semantics. The system may provide means to gather/ask feedbackand/or validate such inferences on videos and/or frames and adjust thesemantic model based on the inputs.

In dynamic environments, the system may need to compensate for thesensing and/or I/O platform movement and as such, semantic artifacts(e.g. leadership) in the current views are coupled with the inferenceand projection of platform movement (e.g. based on trajectory, obstaclesetc.) and thus the system may anticipate based on projected views thefuture inferences, behaviors, scene movement, adjustments and potentialpreservation or change of leadership.

Adaptive modeling configuration consist in adapting the semantic rulesbased on the localizations and lands of the law. Hence once the vehicleis in a location it should adapt its models to the new principlesreflected in the rules of the law. Semantic model roaming is the conceptin which a semantic system updates and/or couples semantic models basedon received instructions, location or based on other semantic factors.

Alternatively, or in addition, the coupled semantic models and/orsemantic profiles can be stored on an internal or external memory (e.g.a mobile device memory) and activated based on various semantics. Inaddition, semantic roaming may comprise updating the biases and/orsemantic factors associated with various semantic artifacts (e.g.semantic rules, semantic routes, semantic groups, semantic hierarchies,models etc.).

As an alternative to model switching, the semantic system containsvarious profiles (potentially organized as groups) of semantics withsemantic artifacts and relationships being factorized in different ways;factors may be derived from semantic analysis of language-based rules.For example, in various jurisdictions the priority of damage to privateor community property might be seen differently; in jurisdiction Acommunity property protection might take precedence over privateproperty while in jurisdiction B this may be the other way around.Hence, when a self-driving vehicle or semantic system passes from onejurisdiction to the other, it should be able to receive from thesemantic infrastructure the new routes and/or rules and update itssemantic model to enforce the semantic rules or semantic routes (e.g.for protecting one property over the other. In an example, this can beachieved through semantic routes of language-based rules; further, thesystem uses association/translation (e.g. based on semantic groups)between the location language and the language of meaningrepresentation.

In a semantic system the semantics for that specific law shall beenabled, re-factorized and enforced while other semantic routes thatconflict with that law should be refactorized, disabled and/orde-enforced. The adaptation to enforcement, or enablement can beachieved through semantic routes, access control rules with variablesemantic factors, factor rules and leadership that are changed based onvarious considerations including the interpretation of the laws. Thesemantic factors assigned/inferred for those artifacts reflect/includethe importance and/or precedence that the semantic system assign to aparticular semantic roaming or other collaborative embodiments. As such,the system may dynamically adjust the rules and leaders based onsemantics and/or location.

During semantic analysis the system may determine strong leaders, softleaders or imperceptible leaders. The system may use such leadership toinfer composite drive semantics and routes including associated factors.In some examples strong leaders may be based on factors that are biggerin absolute value than soft leaders factors; analogously, soft leadersfactors are bigger than imperceptible leader factors. In some examples,the leadership factors are assessed based on orientations and drifts ongroups of leader and/or goal semantic artifacts.

A semantic route can be chosen based on the location and association toa localized law interpretation; the semantic factors of variouscomponent semantics and semantic attributes may vary based on similarfactors.

One application of semantic sensing is robotics and autonomous vehiclesincluding smart post appliances. Full autonomy of vehicles may requiresemantic interpretation of data from various sensors attached to the caror other sensors that are part of the transportation infrastructure.Localization and path identification are a critical aspect ofself-driving cars. The car localization and the localizations of objectsin the semantic field are of importance, while the path identification,semantic routes and semantic composition provides safe driving incomplex environments.

Being able to assess the semantic field and anticipate/project thehappenings in the semantic field ensures safer self-driving andself-determination transportation environment. Car to car communicationand car to infrastructure communications ensure more safety overall.

In an example, while a sensing array (e.g. plurality of sensors; RF,optical, laser etc.) via a semantic suite detects features, markings andother surroundings, the semantic system might control the car to stay ona virtual lane. The virtual lane may be mapped to a physical lane. So,at one stage the car comprises in its semantic view a semantic (orsemantic route) ‘FOLLOW THE LANE 1” while performingspeculative/projected inference on what might happen in the next fewsteps. Based on additional sensor data from surrounding plurality oflocations the semantic system might infer that “FOLLOW THE LANE 1”semantic is not appropriate and maybe “FOLLOW THE LANE 2” might be moreappropriate in the new conditions and hence the semantic system infers a“CHANGE LANE” route and/or command that ultimately changes the highlevel semantic view to “FOLLOW THE LANE 2”. “CHANGE LANE” command isthen translated in routes and/or applied in sensor command and actuationdata; the semantic command is a semantic artifact and as such may have abudget, timed factor and/or linear and/or non-linear signal modulationassociated with it potentially via semantic factors, semantic budgetsand/or plans, in order to optimally execute the command. Therefore, asemantic system may present semantic groups of semantic routes andleaders at any given time in order to ensure safety if the semantic viewgoals or semantic commands cannot be executed within budgets.

A semantic system will incorporate base rules and principles that willultimately derive all decisions of the system. For example, a basicprinciple of the semantic system might be that a “avoid a bad crash”should take precedence over any “property damage”. Hence, the semanticrouting should also incorporate this basic principle in its rules.

The semantic model may incorporate user preferences. In an example,those preferences are based on settings and semantic profiles comprisingsemantic artifacts stored on a mobile device.

In a car example a mobile device can be connected via different meanslike OBD interface to the on-board computer. When a situation thatnecessitates the evaluation of semantic profiles occur, request-responsemessages can circulate via the interface between the on-board computerand the mobile device storing and/or retrieving user settings orfeedback. Users and devices can provide feedback on demand; sometimesthe communication is achieved through semantic infrastructure (e.g.semantic gating, flux, stream etc.).

A communication bus and flux may be used to interconnect multiplesensing devices. A semantic group formation request may bebroadcasted/multicast on the bus and flux and the receiving devicesdetermine whether they will be able to join or form a group based on thesemantic view/view frame that they have. In some examples the broadcastcomprises semantic rules for group formation (e.g. group independent,group dependent) and/or leadership. A semantic wave may be used forcommunications and/or broadcasts.

Semantic groups of devices may communicate on shared environments basedon semantic waves and/or semantic wave collapse. In some examples, thesemantic groups are associated with encryption means within semanticwave analysis and/or collapse. In further examples the encryption may bebased on public/private key assigned to semantic groups potentially in ahierarchical manner. Alternatively, or in addition, these encryptiontechniques may be based on hierarchical semantic analysis.

A semantic view and/or hierarchical level is unchanged if there are nochanges in the inferred semantics and/or leaders in an interval of timeand/or the projection of semantic analysis doesn't yield new semanticsor leaders other than the existing and/or similar ones; however,semantic factors of various semantics and leaders in the semantic viewmay change and potentially determine control commands based on thosesemantic factors and leaders. Other circumstances and elements mightintervene that require changes of the semantic view (e.g. a drivertaking over, artifacts in the semantic field that are not sensed due topoor coverage, types of leaders etc.).

The semantic view change may be assessed on hierarchical levels. Assuch, on a level (e.g. lower level) the semantic view may change,however on another level (e.g. higher level) the semantic view doesn'tchange or only the semantic factors and leaders change (e.g. determiningthe way and order in which rules are applied).

The semantic system may hold and train various semantic units and modelsbased on different rules. In an example those rules and/or drivesemantics may be speculative, antagonized, opposed, complementary or anyother semantic based combination. Those rules may be linked withsemantic factors and the system determines the rules and factors basedon semantic orientation. The system may perform inference on the mainmodels and continuously fuse received feedback from the inference on theother models; the fusing may take in consideration the semanticorientation and semantic drifts between models and drive semantics. Theother models may function on different computing units for optimization.Alternatively, or in addition, the system may perform inference based onall models and use semantic fusion and semantic analysis on theinferences from all models. In other examples the models are coupled,fused and/or gated. In further examples the inference and/or modelcoupling is achieved via semantic flux and gating.

Alternatively, or in addition, the system performs inference on the samemodel using different drive semantics (e.g. antonym, different leadersetc.).

Sometimes, a system may receive and entire semantic model, semanticmodel view (e.g. based on hierarchical levels) or semantic theme modelto be fused or replaced into at least one of its own semantic models.When this is requested or happens, a semantic fusion/exchangemodel/sub-model and/or rules (e.g. gating) may be used to validate ortranslate the received model.

In general, semantic fusion ensures the safety of the semantic exchangesand solves the semantic gaps between various representations and data.

The semantic analysis, semantic fusion and/or semantic gap processingmay use semantic units. Alternatively, or in addition, vector processingunits may also be used.

Segmentation of various aspects of computing and computinginfrastructure achieve better security, reliability and resilience.

Hence in homogenous or heterogenous machine environments the semanticinference and automation can coordinate the segmentation of network,data, functions etc. In a virtual machine environment, the semanticinference may determine the spawning of a new virtual machine on demandin order to deal with an increased workload or a detected threat. Thenew virtual machine can mimic another machine that is being targetedwhile semantic system monitors the new virtual machine for malware,threat analysis coupled with semantic analysis and learning. The virtualmachine may contain means to control various segmentation functions suchas segmentation of the data, I/O, memory, network, functions and thesemantic system controls the security and access control to thesefunctions and segments. Various segments can be assigned varioussemantics and the system control access at these segments and/orfunctions based on the semantic analysis, gating and control.Additionally, the virtual or host machine may have hot plug or plug inpoints or connections which connect virtual logical functions and/orinterfaces to hardware (e.g. achieved via semantic gate and/or semanticflux), thus allowing semantic automation of the resource allocation foroptimization and cybersecurity.

In some examples, the system may want to infer a semantic group thathave (high) energy (or bandwidth, or other indicator) consumption andhas a minimum risk of disruption if its (associated/used) flux channelsbandwidth factors and/or budgets are toggled down, thus allowing thesystem to save bandwidth; once the bandwidth factors and/or budgets arechanged the members of the semantic group may reassess their leaders,views, routes, rules and/or inferences as well so to adjust to the newconditions. In further examples, the bandwidth factors may be based oncost factors and/or budgets at various semantic times; analogously, costfactors may be inferred from bandwidth factors and/or budgets at varioussemantic times. In general, a first indicator factor may be inferredbased on at least a second indicator factor and/or budget.

Semantic systems must comply with a set of hard coded rules that areconveyed via the infrastructure (e.g. land of the laws fortransportation systems) and hence, some semantic routes should beenforced as opposed to other routes. For smart infrastructure semanticbeaconing or smart posts can be used to enforce specific paths androutes. In some examples construction areas may be signaled withsemantic posts and semantic beacons broadcasting construction zone type,factors and other semantics and indicators (e.g. comprising semanticgroupings, instructions, routes, instructions and routes for semanticgroups adherence and any combination thereof etc.).

Authentication of beaconing data is important and hence the ability tovalidate the location is critical; further, the authentication may beaugmented with challenge response inquiries, location information andother authentication techniques (e.g. multiple factor authentication,distributed semantic ledger).

We mentioned the importance of energy radiation and capturingtechnologies in the localization and identification of artifacts andobjects. In these cases, the localization and identification entailsinterpreting the reflected signals from illuminated artifacts, objects,targets, environment and so on. The reflected energy or signals comprisebackscattered or transmitted energy or signals from the illuminatedartifacts and are used for localization and artifact identificationinformation.

Backscattered energy and signals may be used to identify objects and/orobject types based on their radiation signature, scattering, appearance,components, behavior, features, identification and semantic analysis. 2Dand/or 3D images, renderings, frames, video streams may be created fromthese returns as well.

It is to be understood that such images, renderings, frames, videostreams may comprise raw, uncompressed or compressed formats (e.g.bitmap, RGB, HSL, HSV, JPEG, PNG, wavelet, mpeg, quick time, avi etc.).

The semantic engine uses hierarchical threshold calculations andsemantic analysis to capture signals and/or spectral imaging, detectobjects, localize them, associate semantics with the objects in thescene and perform further semantic analysis. In addition, the systemuses such diversity techniques for faster and more efficientcommunication.

By using semantic inference and analysis a system is able to adapteasily to new available intelligence related with its functionalitybecause the system fuses various multi-domain sources of data andinputs.

Semantic segmentation is performed based on the semantic network graphand semantic analysis on the graph. In some examples access controlrules are coupled to the semantic network model to perform segmentationof the endpoints and its mapped artifacts and/or features.

Alternatively, or in addition, deep learning neural networks andtechniques (e.g. convolutional, recurrent neural nets, LSTM) may be usedfor semantic segmentation to tag, score and/or assign confidence forobjects, object types and/or related areas and/or volumes in the 2D and3D renderings and correlate those with the semantic sceneinterpretation. It is to be understood that tags, objects, object types,area, volumes and/or scores/ratings are mapped to semantic artifactsand/or factors.

2D and 3D inference is used for planar, volume and/or artifact(composable and/or composed) printing and/or fitting purposes. In someexamples, the system may infer semantic artifacts associated with areasand volumes and further associated with procedures, technologies andmaterials for printing. If a printer controller implements semanticgating, flux and budgets the system may perform semantic analysis onmanufacturing various parts, assemblies, modules (e.g. for posts etc.)etc.

Semantic analysis and/or localization on 2D and 3D areas and volumes mayassociate and/or relate them with particular actions and/or commands. Insome examples, the actions and/or commands are inferred based onparticular areas and volume semantic artifacts composed with furthersemantic artifacts (e.g. flux, user etc.). The sensors that performelectromagnetic detection may comprise transceivers, transmit unitsand/or receive units. They are coupled or comprise elements such asantenna, lenses, radiative elements, charging/discharging elements andothers. They may comprise elements and circuits including filters,amplifiers, oscillators, resonators, mixers, shifters, phased lockedloops, synthesizers, correlators, voltage adders, frequency/voltagedividers/multipliers, analog to digital converters, digital to analogconverters, SOCs, pSOCs, FPGAs, microcontrollers, peak and phasedetectors, laser diodes, varactors, photodiodes, photo transistors,photodetectors, multiplexers, memristors, semantic units and othercomponents. They may include metamaterials, metasurfaces,nanostructures, nanoantennas, nanowires, nanopillars, nanoposts,polaritons and so forth. The components specified above may be tunableand/or combined to form channels used for transmitting, receive,detection/sense and any combination of those. In some examples, at thebasic level such components may include other analog and digitalcomponents, semantic interfaces, circuits and blocks comprising diodes,transistors, capacitors, inductors, resistors, switching elements—e.g.FET, GaAs, GaN etc.

In an example, a FET (field effect transistor) is controlled in asemantic unit and/or by a semantic unit; a depletion type FET transistoris normally on and to turn it off, a negative voltage relative to thedrain and source electrodes is applied. The enhancement type transistoris normally off and is turned on by positive voltage applied to thegate. Such voltages are controlled by semantics and semantic factors.Further, the semantic units and/or components may be assigned semanticsand/or factors and the system routes the semantic fluxes, semanticwaves, voltages and currents to components based on semantic analysisincluding semantic gating. In a further example a semantic unitdistributes a semantic wave to such components and circuits based onsemantic analysis, gating and routing. Further, semantic channels areestablished based on and for semantic analysis, semantic fluxes, gatingand streaming.

Physical phenomena can also be modeled through semantic analysis. In anexample, Doppler shifts may be modeled through semantics. The radiatingelements transmit generated waveforms which when reflected by an object,artifact and/or target are interpreted based on semantic analysis thatapply the Doppler shift as part of semantic composition. The receivedmeasurement and/or signal from any of the channels and/or antennas arecomposed and/or conditioned based on the transmitted/received waveformswhich may be pulsed and/or continuous modulated in time and/or atintervals of time. Pulse and/or waveform compression techniques may beused for improving the signal to noise and signal to interference ratio.The system may generate the waveforms based on semantic analysis and/orsemantic conditioning. In one example, a waveform is related to acomposite semantic route and/or semantic wave while the compositionalsemantics specify wave type, frequency, amplitude, phase, timemanagement, access control etc. Sometimes the compositional semanticsare directly associated with the outputs (e.g. voltages, chirp, basicwaveform) for the continuous wave and/or pulse signal modulation;additionally, the composite semantic is associated with a semantic rule(e.g. time management, access control, semantic factoring) that willfurther determine additional waveform and/or chirp modulation parametersincluding phase, amplitude and time modulation (e.g. via timemanagement, factoring, indexing etc.). As explained throughout theapplication the semantic analysis and learning implies correlations(e.g. via semantic group, semantic model and/or semantic routes) ofvarious inputs, measurement, signals and so on from various channels,streams and sources. As such, the transmit and return signal parameters(e.g. frequency, amplitude, phase etc.) may be assigned semantics andmay be grouped and/or correlated in time (e.g. learningtime/factoring/indexing management rules and further based on semanticgroups) using semantic analysis; multiple channels and sources may becorrelated this way.

The signal envelope may be inferred, generated or represented based on asemantic network model, semantic artifacts and/or semantic group. Thus,signal envelopes and waveforms may resemble paths/routes/shapes in thesemantic network model and the system performs semantic inference on thesemantics in the path (link and endpoint semantics). The system mayperform semantic orientation and/or drift inference on various semanticwaves, signal envelopes and waveforms for comparison, projection,speculation, inference, sentiment analysis, authentication and so forth.

In some examples, the system may overlay a plurality semantic networkmodels, levels, hierarchies and/or artifacts and infer compositionalsemantics for the artifacts that intersect; the intersections may referto intersections of zones, envelopes, charts, maps, graphics, graphsand/or other plotted and/or rendered artifacts; in addition, oralternatively the intersections may refer to intersections of semanticnetwork artifacts potentially mapped to such zones, envelopes, charts,maps, graphics, graphs and/or other plotted and/or rendered artifacts.Thus, in an example the system may comprise a link Link1 from EP1 to EP2of a level L1 which, when a level L2 is overlaid intersects with a linkLink2 from EP3 to EP4. If the Link1 has a semantic attribute of Attr1and Link2 has a semantic attribute of Attr2 then the endpoints EP1, EP3and EP2, EP4 may collapse and/or be grouped into EP13 and EP24 and anassociated link Link12 between EP13 and EP24 is associated with acomposite sematic attribute between Attr1 and Attr2. Alternatively, orin addition, endpoints EP1, EP4 and EP2, EP3 may collapse and/or begrouped into EP14 and EP23 and linked via a link (e.g. Link 1)associated with Attr1 from EP14 to EP23 and linked via a link (e.g. Link2) associated with Attr2 from EP23 to EP14. In further examples ofsemantic inference on zones, envelopes, charts, maps, graphics, graphsand/or other plotted and/or rendered artifacts the system performsmapping, overlaying and/or analysis on intersections, points and/orzones of interest. In an example, at least two rendered signal/senvelopes intersect in at least one point in time Pint (e.g. potentiallydisplayed as time series charts/graphs). If the system maps EP11 andEP12 to a first envelope/graph/chart (e.g. EC1) and infer and/or assignsat least one semantic (e.g. SEM-11, SEM-12 . . . SEM-1i . . . etc.) tothe oriented links (L11) EP11→EP12 and/or (L12) EP12→EP11 and furtherthe system maps EP21 and EP22 to a second envelope/graph/chart (e.g.EC2) and infer and/or assigns at least one semantic (SEM21, SEM22 . . .SEM2i . . . etc.) to the oriented links (L21) EP21→EP22 and/or (L22)EP22→EP21 then the system may infer composite semantics from thesemantics associated with the links between EP11, EP12, EP21, EP22 inany combination and assign it to the intersection Pint (e.g. and/or anendpoint mapped and/or comprising Pint). Analogously, if Pint isassociated with text, labels, controls, displays and/or other artifactsthen the system may perform semantic analysis based on such artifactsand their associated attributes in rapport with the mapped semanticartifacts and/or further assignment to the mapped semantic artifacts.

The system may display graphics elements based on inferred semanticattributes and/or factors. For example, the system uses an inferredstroke factor and/or semantic attribute to draw the graphs/graphics ofendpoints and/or between endpoints with the corresponding stroke value.

The system may use semantic indexing for indexing user interface and/ordisplay artifacts/controls parameters and/or semantics; further, it mayindex size of borders/fonts, positions, scroll, resizing etc.

Alternatively, or in addition, to the semantic based ingestion andlearning of the semantic envelope, deep learning techniques (e.g.convolutional networks) might be applied to detecting semantics in theenvelope.

The signal and/or noise may be modeled through semantics and thresholdscalculations coupled to semantic inference. Specific formulas may beindicated and/or identified through semantic artifacts; the system mayuse such semantic artifacts in a composite fashion together with thesemantic artifacts associated with the formula parameters. The systemmay adapt the formula semantics based on the context. As such, thesystem may change leaders and/or assign higher semantic factors to asemantic representing one formula set over another based on the semanticview.

The semantic system may use formulas for semantic inference. As such, aformula set may comprise multiple semantics in a composite fashion andmay be part of semantic routes and/or semantic rules.

The system may use semantic representation of knowledge. In one suchexample, when velocity signature estimation (e.g. Doppler) formula isapplied, the system composes the semantics (e.g. including semanticfactors) associated with parameters and constants based on semanticrules associated with formula components. The system uses semanticanalysis in a composite fashion to infer the speed of movement,potentially associated/represented through a semantic factor.

The system may use a mathematical (co)processor to process themathematical functions embedded in the formulas. Such a (co)processormay be connected to semantic units via buses, semantic connects, analogto digital converters (ADC), digital to analog converters (DAC), digitalsignal processors (DSPs) and/or any other technologies mentioned in thisapplication (e.g. FIG. 24 A B C D).

In some examples, the semantic model may comprise rules for matrixmultiplication. Thus, the system comprises rules and routes of typeMATRIX PRODUCT, ADD ALL PRODUCTS OF EACH ELEMENT IN A ROW WITH EACHELEMENT IN A COLUMN, NUMBER OF ELEMENTS IN ROWS—THE SAME—THE NUMBER OFELEMENTS IN COLUMNS. Further, the system may comprise a semantic networkmodel mapped to a rendering of the matrices where the elements inmatrices are mapped to endpoints and the template artifacts that need tobe multiplied are connected by oriented links (e.g. elem (location) 1,line (location) 1→elem (location) 1, col (location) 1; elem (location)2, line (location) 1→elem (location) 2, col (location) 1 etc.); further,the elements of the first matrix (e.g. left product element, left matrix(LM), left matrix element (LME), matrix A, first matrix etc.) lines aremapped to higher level line endpoints (e.g. LME line 1, LME line 2 . . .LME line n, etc.) comprising the line elements endpoints and,analogously, the elements of the second matrix (e.g. right productelement, right matrix (RM), right matrix element (RME), matrix B, secondmatrix etc.) columns are mapped to higher level column endpoints (e.g.RME col 1, RME col2, . . . RME col n etc.) and the system links the lineendpoint with the column endpoints and further may represent and/orcollapse them into a higher level endpoint which may be linked to anelement in the result and/or rendering of the result. Thus, the systemstores a template of matrix multiplication based on semantic models anduses it to perform the product operation for example. It is to beunderstood that the system may infer at least partially such semanticnetwork models by corroborating the semantics artifacts from thecaptured and/or rendered data and its location and by further matchingit against semantic routes, templates and/or rules; in some examplessuch semantic routes and/or rules may be provided, read, received and/orinferred. In further examples, the system needs to multiply AONELINE(11, 12, 13) with BONECOLUMN (11, 21, 31) and thus the system performsthe groupings such as * (11,11), * (12, 21), * (13, 31) based on thematrix multiplication template and further + (+ (121, 252), 403) or +(121, 252, 403) which may map to the result matrix element. It is to beunderstood that the mathematical operations may be performed by thesemantic units in similar templating fashion (e.g. template for numbermultiplication, addition etc.) and/or by a mathematical (co)processorunit (s) as depicted in FIG. 24 . In some examples, the (co)processorunits are linked to the (other) semantic units via semantic flux connectand thus, the semantic unit may use any of the semantic fluxfunctionality to couple and/or challenge the (co)processor unit whichmay expose and/or gate capabilities and/or budgets. Further, the linksand signals between the semantic units (SU) and coprocessor (COP) unitsmay be connected and/or converted by using any combination of analog todigital conversion (ADC), digital to analog conversion (DAC). Further,the (digital) signals on the links may be further processed and/or gatedin digital signal processors (DSP). In some examples, the digital signalprocessor implements the semantic gating functionality (e.g. relatedwith the coprocessor).

A diversity of energy transmitters or transceivers may workcollaboratively to map the semantic field and generate more accurateinformation.

Modalities that use electromagnetic radiation to sense or scan thesemantic field are employed; sometimes they may generate imaging andvideo artifacts of the return signals. These modalities can operate invarious ranges of the electromagnetic spectrum including radio waves,microwaves, infrared, visible spectrum and others; they may include RFsensors, photosensors, laser sensors, infrared sensors and others.

There can be multiple images/frames captured in time in the samesemantic field and/or area.

Sensors can move and the captured areas may overlap, or they can capturedisjoint areas of the field.

Sensors receiving electromagnetic energy in any spectra may usehierarchical threshold calculations (HTC) for object localization;additionally, the calculations may be used to derive a semanticattribute of an object that refracts, transmits, scatters and/orbackscatters received energy from a modality (e.g. camera, laser) viasemantic modulated radiation. Laser/optical type emitters/elements areused to emit radiation, potentially semantically modulated andconditioned, and the number or amount of received backscattered photons,semantic quanta (e.g. energy), backscattered energy, charged energylevels is plugged in as the number of reads in HTC (hierarchicalthreshold calculation) algorithms. When the system uses semanticmodulated transmit signals (e.g. pulsed, chirped, wave based on semanticanalysis), the received backscattered photons and/or energy aresemantically interpreted and analyzed to detect the number of reads.Alternatively, for optical receivers the number of backscatteredartifacts is calculated between subsequent reset semantics.

Photon detection may be based on the energy levels received in aparticular wavelength. Sometimes the photon detection number and/orenergy levels may be associated to semantic factors.

The threshold calculations may be used to identify the nature of anobject (e.g. material, texture, color and others). The systemestablishes thresholds that may be associated and adjusted based onsemantic factors, indexing and further semantic analysis. The factorsmay be based on semantic composition wherein each composition semanticis inferred and/or assigned a factor and the composite semanticweight/factor is a calculation (e.g. sum, average etc. inference) of thecompositional factors. Alternatively, or in addition, the factors arecalculated based on factor rules where the factors vary with thesemantic inference and analysis. In such an example the factors areindexed and/or calculated based on semantic routes, semantic views,semantic intervals, composition, factor rules and plans, semantic rulesand any combination of the former. The selection of semantic rules maybe as such controlled based on inferred semantic factors and indicators.Further, it may just use a weight/factor calculation for one semantic;in an example, a semantic of “SHAKE” with high weight of 0.9 from a carsensor may infer or assign a negative weight to a semantic of “PLEASANT”and and/or a positive weight for a semantic of “THRILL” and/or “FAST”and/or “FAST SHAKE”. Those inferences and factors may be based on leadersemantics capturing sentiments in particular contexts and using varioussemantic profiles. A semantic profile may comprise semantic artifacts(such as routes, models, rules, waves) and allows the system toparticularize inferences and environments (e.g. displays, views,sensing, fields and/or semantic artifacts etc.) based on profile'sartifacts and/or access control. In further examples the semanticfactors are established based on semantic time intervals and/or factorintervals/thresholds. The factors, factor intervals/thresholds may beused to infer semantic artifacts and to select semantic rules, semanticroutes, shapes and groups. In some examples, the system pursues variousroutes of inference based on one or more semantic rules selected throughsemantic inference, factors and factor rules. Further, the system infersand determines which of the semantic rules including factor rules,semantic intervals, semantic groups and other semantic artifacts yieldthe best results (e.g. rewards, budgets) and/or best achieve goals andpotentially update or generate new semantic rules based on continuousinference, action, analysis, feedback.

Photon and counting detection may be an example implementation of theHTC using a diversity of transmit/receive sensor elements. Photoncounting or quantum energy charging/dissipation at a diversity ofelements can be integrated and heavily benefit from the diversitytechniques presented in the HTC. This is due to their susceptibility tonoise which is highly alleviated through diversity techniques and HTC.Further, such sensing elements structures and layouts may be mapped tosemantic layout models (e.g. endpoints mapped on location and/orelements, semantic capabilities, semantic identification, component orany combination of the former).

The diversity techniques and HTC are used to determine semanticattributes of the illuminated surfaces. In the previous example, thephoton count or the energy received in particular wavelengths at theelements are used to derive the semantics related to position and thecolor of the illuminated surface. The elements may be tuned to absorb orcount only a narrow wavelength and the system is able to be more precisein color/attribute estimation.

As explained, there is a clear advantage in designing sensors and sensorsystems that will process information in a hierarchical way by gatheringinformation from sensor components and groups and compose ithierarchically through semantic analysis.

In an example for a photosensor, the photosensor may be comprised froman array of elements or photodetectors that are managed through asemantic engine. The photosensors may be grouped semantically, groupedin a hierarchical manner or any combination of the former. The systemmay perform detection by varying the detection granularity based onhierarchy levels. The mapping of those sensors to the scene may consistsin mapping particular scenes and/or the overall scene or field, withpotentially combining this structure in the hierarchy of logical and/orphysical mapping layers. The semantic inference also uses hierarchies toperform semantic inference.

FIG. 16 depicts elements, sensor or semantic unit components groupedbased on hierarchies and/or semantic groups.

In an example, an endpoint and/or link is associated a compositesemantic based on semantics associated with its component endpointsand/or links. In a similar way the compositional endpoints and/or linksmay be associated to semantics inferred for a higher hierarchy endpointand/or link. In an example the transitions between endpoints at onehierarchy level are allowed, disallowed and/or controlled based onsemantics inferred at higher hierarchy levels. In an example, inferenceassociated with encompassing endpoints at a higher hierarchy level isused to allow, disallow and/or control the semantic inference at thelower levels. Analogously, the semantic collapse may be controlled in asimilar way.

The radiative sensors or sensor arrays may change the radiative pattern,direction, strength, polarization, phase and frequency. The system, maymodulate, represent and/or store semantic artifacts and semantic wavesbased on such values, identities, patterns, attributes and parameters.

A clear advantage of a semantic system and engine is that the radiativefront ends may be easily swappable. Alternatively, the front ends mayuse adaptors to adapt to various transmit/receive spectra, frequencies,polarizations and so forth. In some examples, the adaptors may comprisemultispectral and/or hyperspectral filters.

The system may use readers with antenna elements operating in thevisible spectrum to perform HTC. As such, the radiated energy for suchsensors or interrogators will be in the visible, ultraviolet and/orinfrared spectrum of the electromagnetic domain. In addition, thereaders may have a mix of interrogators or sensors working in variousdomains and/or spectra.

The sensor elements operating in the visible, ultraviolet and/orinfrared spectrum may comprise nano-antennas operating in opticalfrequencies. In an example, the nano-antennas allow the use of readersand interrogators in the visible domain and/or infrared domains.Further, the sensor elements may comprise nanopillars and/or nanoposts.Such elements may be used in electromagnetic radiation (e.g. light)control such as steering, phase control, wavefront control, focal lengthcontrol, dispersion, polarization and other characteristics.

We mentioned before the use of metamaterials in antennas. An example ofsuch nano-antennas and metamaterials are the ones which use surfaceplasmon resonance (SPR), including localized SPR, for detecting light orradiation, usually in the visible domain. Plasmonic materials andstructures have subwavelength properties due to conversion of light tosurface plasmons which allow confinement and concentration of energy tovery small volumes. Surface plasmon polaritons allow the guiding ofincident light of longer wavelengths in shorter nanostructures andwavelengths allowing for nanoscale sized waveguides, detectors and/ormodulators.

Plasmonic materials are used as opto/plasmonic couplers, splitters,photodetectors, switched, waveguides, modulators and so on.

As known in art, plasmonic elements, nanostructures and metasurfaces mayoperate in optical domain for detection of incident light at variousvisible, ultraviolet and/or infrared frequencies; as such they aresuitable for building an optical sensor comprising a multitude ofnanosensors elements gathering radiation at various wavelengths.

Nanoparticles or nanowires are used as sensor elements; their absorptionband in the visible, ultraviolet and/or infrared spectrum and thepolarization sensitivity allow for advanced sensing in small factors; inone example, they can be used to detect various material properties.Accordingly, they can be used with HTC techniques and semantic analysisfor semantic inference.

Nanowires and/or metasurfaces (e.g. based on quantum dots,nano-antennas) may be used for capturing radiation at opticalwavelengths and generating/guiding the polaritons; meshes ofintersecting nanowires are used to capture a current induced by thepolaritons based on direct energy transfer between the nanowires andmetasurface (near field and proximity effects) which may contribute toimproved absorption and detection capabilities in various materialslayouts and applications.

In some examples nano-antennas are built using structures (e.g. pairs,hexagonal structures, other shaping structures) of metallic particleswith dielectric gaps with energy concentrated within the structure or atthe surface.

Gratings and/or meshes of elements may form larger structures andsensing surfaces (e.g. antennas, photosensors surfaces etc.). The systemmay activate and/or tune such elements to achieve dynamic capabilities(e.g. tune the radiation pattern, parameters and/or receiving groupsbased on frequency for optimal transmit/receive; time the elementactivation and/or tuning for controlling polarization); it is understoodthat such activation and tune capabilities may be based on frequency,time intervals (e.g. semantic time intervals), signal amplitude and anycombination of such parameters and/or semantic analysis.

Polarization might be detected by scattering of energy and/or lightbetween nanowires and/or within the mesh.

Multiple polarization interferometry may be used as enhancement to meshmetasurfaces surface plasmons capabilities.

In some examples, dispersion elements/metasurfaces are coupled withabsorption elements/metasurfaces for achieving enhanced capabilities(e.g. focal dispersive guiding, phase detection, spectral sensing etc.).Such meshes may use layouts of one or multiple layers with eitherdispersive and/or absorption properties and elements being used atvarious layers. In one example, a nanoposts or nanopillars layer is usedto capture light and disperse and/or guiding it to a plasmonic layer.Multispectral and hyperspectral sensing may be achieved by controlling(e.g. via semantic analysis) the meshes and/or layers. Further semanticanalysis, 3D mapping and rendering may be used to analyze hyperspectralcubes of captured spectral data.

Frequency and/or photoelectric selective mesh surfaces may operate inthe radio-wave, microwave, terahertz, ultraviolet, infrared and/orvisible range of electromagnetic spectrum.

The RF subsystem may be coupled to optical sensors and devices (e.g.laser diodes, photodiodes, avalanche photodiodes-linear/analog mode,Geiger-mode, etc.; edge-emitting lasers, vertical cavity surfaceemitting lasers, LED, fiber laser, phototransistors) to generate laserbeams and scan the field. A signal can be modulated in amplitude,frequency, phase, pulse/time/width in analog and digital domain that ispotentially used in both RF and optical sensing.

The radio and/or light wave modulations may be achieved based on directsemantic analysis at carrier level or indirect semantic analysis to abaseband level.

Radio frequency and/or optical front-end components may be used and/orcoupled for rf and optical modulation using analogous carrier waves.

In an example, the optical modulation may be either pre-emission or postemission. In the pre-emission (e.g. direct) modulation is achieved bysuperimposing (e.g. compose) the semantic modulated signal (e.g.semantic wave) on the drive current, bias current or diode current (e.g.for LEDs, laser diodes). In the post-emission (e.g. indirect) modulationan optical source (e.g. laser diode, LED) emits a continuous wave whichis then modulated (e.g. via semiconductor electro-absorption,electro-optic modulator, semantic gate etc.) and conditioned. Thus, themodulation may be achieved for example via semantics on or applied oncurrents, voltages, adjustable refractive indexes, phase, frequency andany combination of those. The semantic modulation may be analog and/ordigital. In either one of these methods the optical emissions may becontrolled through arrays/grids/meshes of elements. The system mayencompass array/grids/meshes of modulators (e.g. forfrequency/amplitude/phase pulsed or wave/CW chirpings and orientation inthe field of view). Light pipes, optical fibers, light collimators,nanowires may be used to focus and/or cohere emissions. In some cases,the optical devices may be comprised from a lens or assembly of lenses;in other cases, they may comprise optical antennas (e.g. plasmonic). Thereceptors may include arrays of photon detection elements, photon energycharge pumps, plasmonic nano-sensors etc. By having multiple semanticintegrated front ends (e.g. comprising rf, optical, antennas and/orlens), the readers will be able to perform rf and/or optical scanning ina more coherent manner and perform HTC and semantic analysis closer tothe device's front end while taking advantage of additional diversity insensing.

Photon detectors elements may include photomultipliers, single-photonavalanche diodes, superconducting nanowire single-photon detectors,transition edge sensor elements, scintillation counters, photodiodes,phototransistors and others.

Photosensors may use passive or active sensor pixels; in addition, thesesensors may use organic or inorganic materials. Graphene is a materialused in photoreceptors for improved spectrum sensitivity, resolution andpower consumption.

Photosensors may include multiple spectra capabilities for sensingvisible, ultraviolet or infrared light. Sometimes, this is achieved by amultitude of substrates and/or meshes that are sensitive to a particularspectrum and are activated based on an applied voltage or current. Theapplied voltage may be associated with semantics and/or semantic factorsand the system may use semantic models mapped to mesh substrates toissue semantic commands (e.g. voltage control) to elements in thesemantic model mesh (e.g. edges/links, endpoints, elements, groups)based on semantic artifacts identification, mappings and/or location.The mesh semantic mapping selection may encompass mappings of elementsto the mesh semantic model and selection of those based on the semanticsassociated to the mappings. It is to be understood that the elementsand/or the applied voltage may be associated with semantic analysis.

Alternatively, or in addition, to the multiple substrates physicallayout, the system may map and/or divide a substrate into multiplevirtual substrates based on mapping to the hierarchy in a semanticmodel. Thus, parts of a substrate may be mapped to a level in thesemantic model. The mapping may be disjunct or overlapping betweensemantic network model hierarchy levels, model and semantic artifacts.

Arrays of photodiodes, phototransistors, nano-antennas, plasmonmetasurfaces may be used in photodetectors and photosensors.

It is to be understood that when referring to photodetectors andphotosensors we include any display and holographic display layouts,capabilities and surfaces of based on such technologies.

Photodetectors and photosensors might have different internalconfigurations of transistors, nano-particles and/or components; theymight be organized as a mesh. In an example, a photosensor or group ofphotosensors is organized as or based on a group of plasmon polaritonswaveguide mesh.

Because the elements are sensitive to various spectra, their layout isit therefore of significant importance in sensor applications consistingin a large number of detectors.

In order to improve sensing for hyperspectral photosensors a semanticengine may be used for advanced semantic grouping interpretation andcontrol of the photodetectors or microelements.

Further, the semantic engine may determine the optimal amount of energyvoltage applied to the mesh based on the semantic inference on the meshinputs and other sensorial and/or resource inputs. Further the semanticengine may control the absorption of photons, electromagnetic energy,electrons, and further photoelectrical related parameters in the meshbased on semantic analysis and inference (e.g. time management, accesscontrol, semantic groups, semantic leadership etc.).

It is to be understood that when referring to mesh control, it mayencompass controlling the mesh through the semantic network layoutand/or semantic analysis.

In previous examples we explained various hierarchical and compositetechniques for sensor elements arrays.

In another example a semantic mesh/grid is formed wherein multiplesemantic network models are laid down on top of each-other; the stackedconfiguration may form a logical and physical hierarchical layout. Assuch, the links may intersect and the semantic system defines newendpoints at the link intersection and assigns new composite semanticson the new endpoints and links. The composite semantics may becombination related with the semantics assigned to a lower or ahigher-level links and/or endpoints, and potentially with semanticgroups of endpoints. Thus, the system enhances the semantic mesh grid toencompass finer and more granular understanding of semantic scenes andfield. The system may pursue finer semantic grids when focusing onparticular areas/locations, goals, leaders, drive semantics and factors.The semantic grids may be formed in layered and/or hierarchicalconfigurations. The layered and hierarchical approach increases thesemantic resolution (whether disjunct or overlapping) optimizesperformance, knowledge transfer and control. In an example, two gridscommunicate through a higher level in the semantic network model. Such,architectures may foster domain knowledge transfer between microgrids ofelements, layers/hierarchy and/or endpoints. In an example the systemperforms up (e.g. abstract, higher level, connected level) and downinferences within the hierarchy based on goal inferences. The sameapproach works for any embodiment of the semantic network model. In someexamples the semantic network model was mapped to a grid of sensingelements. In other examples, the semantic network model is mapped tolocations and/or artifacts in images/frames (e.g. pixels, objects,zones, shapes, boundaries etc.).

Some of these examples were based on semantic analysis includingcomposition, semantic routes, time management, access control, ratingand weighting, diversity drive/routing, semantic leadership,hierarchical and probabilistic approaches. We mentioned the use ofsequencing and semantic factors (e.g. weights and/or ratings) forincreased selectivity when applying semantic inference rules, inferringroutes and semantics; as such, the semantic rules, routes and semanticsare ordered and/or selected based on semantic factors and semanticfactor rules and may determine and/or be determined based onorientation, drift, sequencing and/or other semantic analysis. Thefactor rules are created and updated based on inputs and feedback from avariety of sources. Sometimes those factor rules are updated based oninferred semantics, inputs from a user and/or any other sources aspresented in this specification.

Those factor rules may themselves be associated with semantics and thefactors associated with the semantics representing the selection factor.As such, the semantic inference techniques are used to infer new factorsand factor rules, infer semantic groups including factors and factorrules and so on. In the case that there are multiple semanticsassociated with a semantic group (e.g. of rules, artifacts etc.), thesystem may perform semantic analysis on the multiple semantics and inferthe overall prioritization, selectivity, importance factors and/orleadership.

The semantic indexing factors establish space-time dependencies based onsemantics. Thus, the sensing elements (e.g. photodetectors) may bemapped and/or grouped based on semantic space-time indexing.

The system may determine a coarse semantic determination at first and gothrough the logical and/or physical semantic hierarchy until a semanticthreshold and/or leadership is achieved. The system may increase theresolution of the semantic determination through semantic indexing; assuch, in a vision model (e.g. optical, rf) new semantic artifacts areadded to a semantic model mapped to the semantic field representation(e.g. time-space field of view) based on the semantic indexing as thesystem increases the resolution of the semantic model; the increasedresolution may target a particular granularity, particular semanticinferences, rewards and/or other goals. Also, the increased resolutionmay target semantic scenes, semantic groups, leaders and/or any othersemantic artifacts. In some cases, the targets may be associated withleadership status.

In the case of mesh mapping and/or hierarchical semantic models thesystem may use various layers of the mesh and/or model to achieve aparticular desired resolution. The semantic indexing factors may be usedto determine the progression in resolution and time of mesh/modeladjustment, activation and semantic inference. In some examples, thesystem uses indexing to infer semantic groups of elements and performzooming and/or adjust resolution (e.g. as a result of progressivesemantic compression/decompression and/or encryption/decryptionpotentially based on semantic wave).

The system may overlay models (e.g. mapped to pixels, elements) andcreate new artifacts based on color. Alternatively, or in addition, canoverlay models and determine composition and analysis on the overlaidmodels.

In another example, the system maps a grid of endpoints and orientedlinks to the semantic field and increases the grid density throughsemantic indexing or, in a further example, the system overlays orenables/activates semantic grids on top of each other based on semanticinference and detects intersection points between the semantic artifacts(e.g. endpoint and links). The system may determine the intersectionsincluding oriented links and/or endpoints (e.g. source and destination)and, at intersection points, the system may map new endpoints and createnew links based on the composition of the semantics intersecting in thecomposed grid. Semantic groups may be used to determine the new mappedsemantics in the grid; semantic composition may be used to determine newsemantic groups based on the new determined semantics, sensor elementsmapped at locations and other semantic artifacts. The system usessemantic orientation (e.g. based on drive semantics and/or leadership)to detect drifts and patterns between layers, routes, shapes, paths,trajectories etc.

The system is able to infer indexing factors based on mesh overlaying.As such, if there are two endpoints and a third one is overlaid inbetween the first two endpoints the system may infer proportion andindexing semantics based on the semantic layout, mesh/grid, links,hierarchical structure, semantic factors, semantic shifts, drifts andsemantic orientation. Further, the system uses semantic inferenceincluding localization within semantic scenes and field semanticcomposition, orientation and shift for learning indexing, factor, rulesand other semantics.

Semantic view frames comprise semantic determinations and semanticroutes that may be used by the system for semantic inference.

The system may use a semantic bias for altering the factors forparticular semantics, fluxes, routes, view frames and/or views. A biasmay be applied (e.g. composed) on drive semantics, semantic routes andother semantic artifacts. Alternatively, the bias may be applied as analternate or additional drive semantic, semantic route, semanticartifact and/or leader. Factors associated with the semantics in thesemantic route may be biased; this bias may be used to ponder the othersemantic factors including route components semantics. The semanticbiasing may be inferred based on semantics, indexing, semantic analysisor be based on inputs including inputs from a user.

The semantic bias may be used to influence (e.g. counter-balance,control, increase) the confirmation bias, the risk aversion or riskpredilection bias that may reflect in the semantic model and/orcollaborative semantic fluxes.

The semantic bias may be used for example to identify signatures,compatibility, preferability, trusts and other semantic factors betweensemantic groups of artifacts, units and/or fluxes by evaluating (e.g.via composition, orientation, drift, shift, coherence/decoherence andfurther analysis etc.) the semantic factors, artifacts, routes and/orviews used by such group (members) during a particular inference and/orchallenge; it is understood that such analysis may be effected onsemantic groups and/or between the evaluator system and further units,fluxes and/or semantic groups. Further, such analysis may be used toinfer semantic groups and further semantic artifacts as explainedthroughout application.

The system may use aggregation of semantic biases (e.g. by semanticanalysis) of/for various semantic groups, fluxes and/or components whichmay be used to assess the compatibility, preferability, trust, spreadand/or other factors in relation which the corresponding semanticgroups, fluxes and/or components.

In further examples the bias may be used to compensate for variouslanguage and sensing accents (e.g. based on semantic identities),identification characteristics, sounds, waves, noise, parameterizedcharacteristics, signals and/or other artifacts that may have aninfluence on increasing detection factors (e.g. related to signal tonoise, signal to interference, superposition and so forth), In someexamples semantic biases and semantic indexing coincide and as such anysemantic techniques applicable to one may be applicable to the other.

Semantic indexing, bias, access control, gating, time management and/orfurther rules may be associated and/or used to adjust biasing voltages,currents and/or further bias parameters; further, semantic inferenceand/or learning based on correlations between biasing parameters and/orvalues and changes in operating characteristics of (biased) elements mayoccur. In further examples, semantic resonance, decoherence and/ordamping may be used to determine operating points/intervals. Thus, thesystem may adjust, control and/or optimize inference, gating, operatingpoints/intervals, actuation, motion, power (budget) delivery, torque,(rotational) speed etc.

Sensors, arrays, grids, mesh of sensors including photosensors may useall the techniques previously presented for input interpretation.

Some of the existing photosensor take snapshots of the scene at intervalof times and interpret the data based on various techniques includingdeep neural networks such as convolutional networks, recurrent neuralnetworks, long short-term memory networks and others. In general, deeplearning techniques are not very efficient because they need to filterand/or interpret the data in a repetitive manner and as such areprocessing intensive while not being able to have a continuous semanticawareness.

Another approach is to dynamically control the photosensors and/or meshbased on the understanding of the environment and optimize the sceneinterpretation. Groups of photodetectors may be coupled together in aconcentrator/controller/semantic unit and be coalesced and/or controlledthrough that semantic unit; further, those photodetectors may beassociated with a semantic unit group.

The elements in a cell can be connected via nanowires, with the controlof the voltage threshold of the nanowire circuit or transistor beingsemantically achieved. Semantic units may be also connected to othersemantic units and/or photoreceptors; they may be connected and routedthrough semantic flux, gates, routes etc. The semantic units run asemantic component which composes the sensor inputs semantically whilebeing controlled by semantics itself. Further, the elements in the cellsmay be composed in semantic groups.

The semantic units may comprise at least one semantic cell. A semanticcell may or may not comprise at least one conditioning semantic unitfront end block (SU FEB) (e.g. FIG. 19 A B C). Examples of semanticunits and cells are depicted in FIGS. 21, 22 and 23 . A semantic cellmay comprise SU FEBs in a switched and/or hierarchical architecture(e.g. FIG. 21, 23 ). FIG. 21 shows an example of configuration of aswitched architecture while FIG. 22 shows an example of a semantic unitcell block. While those examples comprise semantic components such as SUFEBs (semantic unit front end block), SU CELL (semantic unit cell),semantic unit cell block, and SU (semantic unit) it is to be understoodthat in some cases they may be used interchangeably as architecturalelements in diagrams and examples, the reason being that the semanticarchitecture is hierarchical; further such components and their linksmay be mapped to semantic network models and may use semantic waves forcommunicating semantic information. In general, a semantic unit is ahigher-level semantic architectural artifact which may or may notcomprise any of the other semantic components; further, semantic unitsmay comprise other hardware elements, components and/or blocks (e.g.storage elements, I/O etc.) that may implement semantic and/or otherfunctionalities and/or protocols. It is to be understood thatcombinations of semantic artifacts and/or components whether disposedand/or configured in a hierarchical layer architecture and/or semanticflux architecture may be used to form semantic memories. Semanticcomponents and/or artifacts may comprise any number of input and outputsignal interfaces that may be interconnected and used to controlvoltages, currents, impulses, clocks, discrete or analog inputs and/oroutputs, or semantics to other semantic components, computer/semanticunits which interpret the data/signals based on semantic analysis asdescribed in this application. As such, the propagation through thesemantic architecture is used in semantic inference potentially usinghierarchical semantic network models.

A single or a plurality of photodetectors may be connected to a semanticunit. Alternatively, or in addition the photodetectors are connected tomultiple semantic units.

The semantic units may include transducer and/or transducing components.Further elements associated with a semantic unit may performphotoelectrical emission detection.

The connection and layout of elements and semantic units may bereconfigurable. As such, the elements and units connections arereconfigured in semantic groups based on grid/mesh control semanticnetwork layout, hierarchical overlay and/or semantic analysis.Multiplexer, demultiplexer, switches (e.g. crosspoint) components andcombinations may be used for connection reconfigurability withinsemantic components and architecture. Such components are depicted inthe examples of FIG. 21, 22 as MUX. It is to be understood that suchcomponents are used to interconnect semantic components in variousconfigurations whether one to one, one to many, many to one or many tomany. Such components may be either analog and/or digital and becontrolled via semantic means (e.g. semantic, semantic waves etc.).

Instead, or in addition to, of photoreceptors, sound and/or pressurereceptors may be used in such semantic sensing apparatuses.

In yet another example, other types of transducers are utilized insensor and apparatuses for radio frequency sensing, optical/photon basedcommunication mediums, sound/ultrasound sensing, biosensing and others;application of these apparatuses may vary from communication, quantumcomputing, localization, proximity sensing, medical imaging, medicalapplications, DNA sequencing, gene identification/characterization andprofiling, networking, cyber security to other applications.

A photodetector detects incident light/photons and transduced signals(e.g. current) are transmitted to and/or through the semantic unit. Thesemantic unit uses its semantic model, semantic engine and/or circuitryto determine if need to route and/or to control adjacent semantic unitand/or photoreceptors.

A semantic unit may communicate with other semantic units in order toperform semantic inference and/or excite or inhibit other semantic unitsand/or semantic memory. The communication may be achieved throughsemantic gating, flux, routing and/or waves.

The photosensor may have a multitude of substrates with each substrateincorporating interconnection links between various elements of theprevious substrate. As such the photosensor structure may resemble ahierarchical mesh which may be mapped to a semantic network model. Thus,design and assembly tools and techniques based on semantic inferencemesh and/or on semantic network models (e.g. mapped to locations,elements and/or hierarchies) may be used for sensor design and couplingsbetween the sensor elements and layer hierarchies.

If there are multiple modalities capturing the same areas or areas thatoverlap in the semantic field, then the semantic fusion uses all theimaging artifacts from all these modalities in order to improve thesemantic field object detection and identification tasks throughsemantic analysis applied to semantic mesh and semantic network model.

Sometimes only a number of objects/features in a scene are of interestand the other artifacts gated or considered noise. Semantic routes,views, view frames, factors, leaders and biases are used forconditioning, selection, gating and/or to reject noise. The system mayuse refocusing/retuning of the sensing elements or entities to increasethe signal to noise ratio; in an example, once the system recognized twoobjects, one of interest (maybe because has a leading semantic attributewithin a leading semantic route) and another one not of interest, thesemantic engines commands the sensing to focus, increase granularity(e.g. low level mapping and inference), map on the object of interest,while potentially instructing the mesh to reject, factor and/or biassemantics associated with the non-interest objects or scenes.

As presented, the system uses adaptive localization of artifacts and usethe semantic models to track their movement in the scene. As such, themovement and location of features, artifacts, types, groups and objectsassociated with semantic attributes are interpreted and/or trackedcontinuously using semantic analysis and control; the systemcontinuously adjusts the semantic models based on semantic analysis.

A semantic network model can be mapped to data, frames, images and/ordata renderings from the sensors based on location; endpoints, links andsemantic groups of artifacts are potentially mapped; further, thesemantic network model is mapped to the location and/or identificationof the sensors in the sensor array or grid. The system may map theendpoints directly to the array and grid of sensors and sensor elementsvia location and/or identification. Alternatively, or in addition, thesystem may map the semantic model artifacts based on components and/orgroup identification and/or semantics. Mappings may be one to one, oneto many and many to many; the system may use semantic groups to performthe grouping of sensor elements either as they are represented andmapped as one or more endpoints and/or links. The system may mapsemantic groups of network elements to semantic groups of sensingelements.

The system maps semantic groups of elements to the semantics and/orhierarchical semantic artifacts based on learning from other modalities(e.g. voice).

In an example, the photosensors are basically capturing the semanticfield and their location, orientation and/or identification are directlycorrelated with the location of features, objects and/or semanticscenes. In some examples, the system uses stereoscopic vision, depthcalculation and other passive and active technologies and techniques.

In the case that the system uses mapping of semantic network modelartifacts (e.g. endpoints and/or links) to the sensor grid elements, thesystem may map an artifact to a group of elements and as such thesemantic inference on the group may be associated with the artifact.Alternatively, or in addition the inference on the artifact may beassociated and/or translated to a semantic group. Further the system mayuse hierarchical transformations on artifacts to represent groups,causality and other relationships. The system may use semantic inferenceat an artifact and as such a semantic group of elements. Also, because asemantic feature may be comprised across the artifacts the system usesthe hierarchical network semantic model to detect/compose the feature ata hierarchy level and associate the feature with other semantics in thenetwork model based on the semantic routes between the endpoints of thesemantic group comprising the feature.

The system may use semantic orientation for comparing and fusingfeatures, frames or scenes.

Semantic artifacts may be associated with endpoints and/or links whetherin a hierarchical or non-hierarchical manner.

Further the semantic artifacts (e.g. semantics, semantic groups,semantic routes, shapes, views etc.) may determine the mapping of thesemantic network model to the grid of elements. In one such example themapping is determined by the correlation and/or inference between thesemantics artifacts in the network model and the semantics artifactsassociated with the elements in the grid.

Semantic trails, semantic routes and shapes are used to represent/conveypattern matching between semantics, sensing elements, mesh/grid layoutand semantic artifacts at any layer of a hierarchical semantic networkmodel or between layers and hierarchies.

The system may use semantic shaping and/or hierarchical semantic patternmatching to identify common artifacts, areas, locations and/or semanticgroups between frames and/or images; such artifacts may be used asanchors. In some examples, based on the anchoring of processed frames onat least one artifact the system may calculate indexing factors used toreorient and/or focus sensing artifacts (e.g. cameras); in furtherexamples such indexing factors may be used to actuate mechanisms,motors, spinners, springs, stabilizers, shocks and further attachmentelements of the sensing components to the chassis bearer.

A semantic network model can be composed from a plurality of sub-models;the sub-models may be ingested from various sources (e.g. a semanticflux), may comprise semantic rules with different biases andorientations, may represent various themes, may be associated withparticular artifacts and so forth. They may be distributed and/or fusedat any level of the semantic network model hierarchy.

In an example, the system recognizes the semantic of an image orsemantic scene in a hierarchical fashion. The system detects varioushigh-level semantics that are used to route the semantic inference atlower levels in the hierarchical semantic model. Further, the semanticinference may be routed between layers in the hierarchy based on thesemantic field and scene developments analysis. The system may controlthe sensing elements based on the semantic analysis. In an example,high-level semantics may be determined from a coarse or fast assessmentof the semantics at lower levels. The system may perform inference inany direction and/or patterns in order to improve semantic accuracy andgranularity. The patterns may be associated with semantic routes, shapesand further the system performs semantic orientation and patternrecognition based on leadership status. The patterns may be related withabsolute or relative directions and orientations in a composite fashionwithin the hierarchical semantic network model.

The system may use indexing of semantic network artifacts, to determineand preserve the scene development. In an example, the system usesintermediary and/or indexed mapping of model artifacts to determine thata car has the color brown by evaluating the car chassis visual modelfrom left to middle of the car and further middle to right. Thus, thesystem splits and maps the original shape/area/data/text to modelsub-artifacts and perform inference on sub-artifacts. The semanticrelationship between the original artifact and/or sub-artifacts may berepresented as semantic groups and/or sub-models. The inference onsub-artifacts may be composed potentially in a hierarchical manner andassigned to the original artifact. The sub-artifacts may be mapped basedon semantic indexing of the original artifact. Model artifacts compriseendpoints, links, sub-models and other semantic artifacts.

In the previous example the system may have detected that endpoint A andB are shades of brown and that a link between them is CONTINUOUS FADINGCOLOR so that by using semantic analysis the system may have furtherinferred brown shapes. Further analyzing other features and hierarchicalartifacts at hierarchical levels infer BROWN CAR AT C, SIGNAL WHITE,DELOREAN SIGNAL ON, SIGNAL OFF, SIGNAL BROKEN, JOHN'S DELOREAN etc.Thus, the system is able to identify artifacts at any semantic level(e.g. CAR, DELOREAN, JOHN'S DELOREAN) based on semantic analysis. Thus,as explained throughout this application, the system is able to usesemantic identities in a routing, gating, orientational and/orhierarchical manner in order to guide the semantic inference of semanticidentities. In some examples, the system is allowed to pursue semanticidentification based on gating and/or access control (e.g. allowingparticular semantic groups and/or semantic identities to pursue semanticidentification at various levels).

The system uses location, semantics associated with locations and timemanagement rules to infer semantics associated with (semantic)identities. In some examples, the system observes a location which isassociated with a social event based on a time management rule and thesystem further has a goal of OBSERVE DE LOREAN CAR based on a LIKE DELOREAN factor and a semantic route of LIKE DE LOREAN, OBSERVE DE LOREANand further (JOHN) DE LOREAN SHOULD ATTEND EVENT. The system furtherinfers that DE LOREAN NOT PRESENT until infers that DE LOREAN PRESENTbased on the identification of the DeLorean car through sensing means orbased on semantic flux and/or inference on other data; it is to beunderstood that DELOREAN IS PRESENT holds a confidence level based onvarious factors such as risk in (poor) identification on (JOHN) DELOREAN(owner), semantic flux risk and/or further inference factors. Once theevent finishes the system learns that the semantic identifications ofDELOREAN is linked through PRESENT (or ATTENDANCE, or other semanticgroup member) in relation with the semantic identifications of theevent. The system may use leadership semantics (e.g. DELOREAN) andsemantic identities and/or groups comprising the leadership semantics(e.g. JOHN, DELOREAN; JOHN'S DELOREAN) in order to further match timemanagement rules associated with such semantic identities, semanticgroups and/or members thereof (e.g. in the previous example the systemmay have known that JOHN DELOREAN need to attend a vintage car and/orDELOREAN car event and as such it may have adjusted the factorsassociated with the presence, identification and/or location of the(JOHN'S) DELOREAN car and/or JOHN DELOREAN).

The system may decay the factors associated with the learned semanticartifacts and thus giving them less priority in inferences;alternatively, or in addition the system moves and/or copies the learnedsemantic artifacts to other areas of the semantic memory. It is to beunderstood that the decaying of factors is based for example on thefactors associated with LIKE DELOREAN sentiment (e.g. decays less if thefactors associated with the sentiment are high and decay more is thefactors are low).

Noise or unwanted signals, waves, envelopes, graphics may be detectedand isolated via semantic analysis and may be filtered via signal and/orsemantic processing techniques. Signal, wave, envelope, graphic,filters, conditioning and identification may be achieved via semanticconditioners. The sematic conditioning can be done by specializedhardware and software components (e.g. semantic units) or can beachieved through more general-purpose computing modules including fieldprogrammable gate arrays, GPUs, CPUs and others.

In an example the conditioning is based on semantic orientation, shapingand semantic drift analysis on signals, waveforms, information, graphs,routes, shapes, semantic views, view frames, models etc. Further, thesystem may compose the conditioning and/or noise, potentially with otherinferred semantics, and further condition them.

The process of semantic conditioning, composition, analysis andorientation can be done any number of times at any level or betweenlevels of a hierarchy. The system may determine complex behaviors,patterns and orientations based on such techniques.

The semantic fusion can be done by using unconditioned imaging andsignals, conditioned imaging and signals, can use various features,object and groups identification techniques. Alternatively, or inaddition, the system may use semantic analysis and conditioning with oron noise signal.

Image analysis can use various sampling techniques includingoversampling and under-sampling with the semantic conditioning andfusion techniques.

The conditioning can use analog and digital techniques coupled withsemantic analysis in order to perform inference. Analog to digitalconversion and digital to analog conversion may be coupled to semanticanalysis and/or semantic conditioning.

Various feature detection techniques can employ single, combinationand/or multiple stage algorithms and techniques; some may be based ongradients, divergence, nearest neighbor, histograms, clustering, supportvector machines, Bayesian networks, entropy (e.g. maximum, minimum andrelative; whether quantum and/or statistical), deep convolutionalnetworks, long short-term memory, recurrent neural networks, and others.Combinations of these techniques with semantic analysis may be used; itis understood that such techniques and their formulas may be modeled insemantic models and rules.

The system performs real time statistical analysis on real time semanticroutes wherein the system performs statistics based on semantic analysison the route. In some examples, the system determines statistical healthfactors given particular routes and/or habits.

In general, deep learning network systems are only relatively efficientfor feature extraction and recognition since they don't considersemantic analysis and thus, they require fairly high computing power andin the case of supervised learning require large amounts of trainingdata; even so, the processing is not always achieved in real time. Asemantic engine may couple any of former techniques with semanticintelligence and analysis.

Semantic model artifacts may be associated with gradients. In anexample, color or grayscale gradients of an image and/or frames areassociated to artifacts (e.g. oriented links) in a semantic networkgraph. In one example the system performs drive semantic or orientationinference based on semantic groups which correspond to features, colorsand/or gradients semantic patterns.

In further examples, at least a layer in the semantic model may bemapped and/or associated with a vector field. Further, the divergence ofthe vector field is used to determine semantic factors associated withthe inferred semantics in the semantic model.

In an example of a self-driving car infrastructure the vector field maybe associated with the entropy of semantic group of cars travelling in aformation and/or mapped to an endpoint and/or area. It is to beunderstood that the entropy of the semantic group may be related to avariety of conditions and artifacts including trajectory entropy, volumeand/or area entropy, topological entropy, semantic drift entropy,encoding, behavior entropy, intention entropy and/or signature entropyetc. Further, the entropy may be further related and inferred based onendpoint and/or area semantics (e.g. based on sensing, weatherconditions etc.), semantic drifts and so forth. In addition, based onendpoint entropy and/or divergence the system may perform semanticanalysis including inferring new endpoints and/or links in the semanticmodel. Further, the system may infer optimal safe trajectories and soforth; it is understood that the system may use optimize trajectories onmultiple goals, factors and indicators such as car capabilities, safety,comfort, entropy, energy consumption and so forth.

In further examples, the vector field and/or semantic network modelhierarchies may be used to infer, associate and/or apply torque(vectoring) to the drive wheels of semantic post/s or other vehicle/s.Thus, the semantic torque vectoring may provide superior body rollcontrol by including an exhaustive set of conditions and circumstancesfrom a variety of sensors, fluxes components and/or layers in thesemantic model. In some examples the torque vectoring is inferred basedon current and/or projected conditions and circumstances (e.g. at thelocations as mapped on the driving surface area and further based onparameters of embedded sensors in the tires—used to infer semanticattributes about tires, road surface etc.—and/or about tires, systemcyber condition, driver condition etc.).

A semantic group may be conditioned, gated, composed, reconstructed fromand/or deconstructed in multiple semantic groups based on semanticanalysis of the group's semantics and semantic inferences. In anexample, a user may pose a challenge to the system and the systemperforms inference based on the challenge. The challenge may be forexample in a semantic structured form and/or natural language. The usermay specify goal leader artifacts and factors. While semantics mayexplicitly comprise those artifacts, in other embodiments the semanticsystem also infers them based on further semantic analysis initiatedinternally.

In some cases, the system infers goals and routes for a response. Forexample, a user or a collaborative system may ask a question “is thissweet?” and the system is able to perform goal identification-basedinference for “sweet” drive semantic and the previous inference on thecontext. The system is able to couple this with a previous inference ofan object that formed semantic trails, routes and composite semanticssuch as “APPLE RED GRAY LINES AT THE BOTTOM” “SWEET 50” “VERY SWEET”“FAVOURITE 100” “BEST APPLE” etc.

The system may form semantic groups based on semantic analysis andsemantic linguistic relations. In an example, group composition may beimplemented based on the synonymy of semantics that define and/or areassociated with at least two semantic groups. In a further example, thesystem may form a composite semantic group comprising only the semanticsthat are synonyms at the group definition semantic level and/or groupmembership semantic level. The synonymy may be determined based oninferred semantic factors, indicators, routes and/or semantic viewinformation. Thus, compositions supporting particular goals, factors,orientations, shapings (e.g. shape-based inference) and/or indicatorsare based on such techniques. In other examples, they may be based onantonymy; further, any other semantic linguistic relation may be used toemulate composition between semantic groups. Further, the system mayinfer and assign factors, indicators and/or semantics to newly formedgroups based on semantic analysis. Such techniques may be used toperform semantic analysis for drive semantics, semantic routes, viewsand other semantic artifacts based on semantic chain development.

Semantic groups composition may be used for semantic orientation,shaping and/or drift. For example, the system may calculate orientationand/or drift between two semantic routes.

The semantic groups formations are also location, time and/or semanticartifact based. In an example, they may be modeled/represented assemantic network graphs where any causal relationship ismodeled/represented as an oriented link between two semantic artifactsthat share the causal relationship. In an example of two entities A andB a causal relationship may be “A THREAT TO B” or “A INFECTED B” and assuch the system represents the causality as an oriented link from A toB. The oriented link may be assigned a semantic of THREAT or INFECTED oralternatively, or in addition, an upper hierarchy artifact maydetermine/specify the causality relation via its associated semantics.

A semantic system uses semantic clustering of data in a memory (e.g.semantic memory) for efficient access, inference and rendering of mappedimages and frames. The semantic clustering is based on semanticanalysis, semantic model and semantic groups. Additionally, the systemuses location clustering and time clustering analysis based on semanticanalysis, semantic network models and any of the techniques explainedthroughout this application.

Sometimes the locations are associated with artifacts at that locationand as such the system performs groupings of the artifacts based on thesemantics associated with the locations and/or the links betweenendpoints associated with those locations.

In an example, the system ingests image, video frames, tactile, pointingand/or other inputs (e.g. from a user). As such, the system maps thenetwork semantic model to the renderings/frames/data and performssemantic analysis to determine semantics and or semantic groups.Further, the oriented links between endpoints associated with semanticgroups in the semantic network model may be adjusted based on semanticanalysis. Alternatively, or in addition, inputs from a user or othersources may be used to setup, determine or adjust the semanticsassociated with the semantic network model. Further, users may posechallenges and the system performs inference based on the challenge. Thechallenge may comprise a specified and/or inferred goal—e.g. performinga transaction with moderate risk and moderate cost in a period of timeor, “buy a track ticket for 10$ until breakfast tomorrow”; it isobserved that in the last semantic construct the indicator risk isimplicitly inferred as the semantic analysis and/or route progresses(e.g. the system may not poses the idea of the risk of not attaining thegoal until analyzing the end of the construct and/or during the semanticgoal development) and thus the system infers risk factors and indicatorsbased on semantic budgets (e.g. time, cost etc.). In such an example thesystem may generate a semantic route and/or semantic rules based on theinferred semantics and semantic time. Thus, the composite transactionsemantic may be associated with semantic time intervals comprising theinference of “breakfast” and “tomorrow”. Further, it may be associatedwith semantic intervals comprising a semantic flux monitoring, ticketprovider sales and the target price. It is to be observed that the priceand/or time target is a semantic goal related to semantic budgets of thecomposite or route main goal. Based on further semantic analysis (e.g.based on the challenger's funds/budgets, availability/supply, trackevent attributes/purpose/goal/identity, track semantic time constraintsetc.) the system may prioritize one budget (time, cost) over the otherand/or factorize one in rapport with another (e.g. time to cost factor);it is understood that the semantic analysis may comprise past, current,speculative and/or projected semantic artifacts. It is to be mentionedthat the system may have a variety of registered track providers (e.g.via fluxes) and the system may select one (e.g. challenge/issue/commandan offer, purchase order and/or purchase semantic for a particularsemantic identity using a payment processor and/or secured budgets)based on the goals and/or other ratings coupled with semantic analysis.Further, the system may use indicator biases (e.g. risk bias;desirability bias comprising desire, worthiness etc.) to control thebehavior (e.g. index and/or factorize budgets, hysteresis, damping,diffusion, routing and/or drifts—etc.), trajectory and/or route entropy;further factors such as desirability to risk composable factor,desirability to risk composable routes and other composable artifactsmay be used. Analogously, the system may determine carriers, providers,posts, vehicles, routes and/or groups thereof for movement, shipping,receiving, logistics etc.

In similar ways the system may implement semantic contracts whereincontracts are ingested as a sensed free form, text file, specializedform document, XML file and/or other fields and formats. The systeminfers clauses (e.g. goals, indicators etc.) based on semantic analysison the contract. Once the system infers further semantic artifacts itupdates the status (e.g. factors, indicators) related with the goals(e.g. SHOE DISTRIBUTOR A RISK 90% OF LOW SHOE SUPPLY). Further, thesystem may have a rule that specifies that the previous status compositesemantic may be coupled with an automatic order to DISTRIBUTOR B whilewithholding payments to DISTRIBUTOR A based on factor plans related topayments; the withholding of payments may comprise paying only partialsums based on payment plans; such payment plans may be associated withtime management, budgeting, factoring, indexing and any other semanticrules It is to be understood that the system may be connected to atleast one payment processor potentially via a semantic flux.

In further examples the semantic system is connected and/or comprisinglive feeds and/or semantic fluxes associated with financial markets,trading, stock indices and/or other financial instruments. Thus, thesystem may issue trading and/or stock orders based on investment goals,associated fees, target asset allocation and diversification. It is tobe understood that the investment goals may comprise reward to riskfactors, budgeting and/or further factorization. Also, the associatedfees may be used as budgets associated to semantic indicators ofparticular trades, stocks, indices, trades/stocks/indices type and/orstatus, and/or semantic groups thereof. Also, the diversification may bebased on entanglement entropy of particular trades in respect to factorsand/or parameters such as domain, valuation, rating, leadership,seasonal (e.g. based on time management), budgets, revenue, trend (e.g.potentially mapped in the semantic network model) and/or otherparameters. The system may use damping factors and/or rules to issuemarket orders wherein the damping equilibrium is the target goal (e.g.acquire a particular budget).

During trading, inference and/or execution the system may encounterdelays caused by infrastructure which may trigger decaying of budgetsand further semantic analysis. It is to be understood that theindices/stocks valuations and/or graphs may be mapped into semanticanalysis based on (interval) thresholding and/or (overlay) semanticnetwork models.

A semantic system uses an adaptive semantic model and continuousinference of semantics in order to interpret the semantic field. Thesemantic field may be bound to sensorial inputs and/or any other source.Semantics may be associated with general vocabularies; sometimes morespecific vocabularies incorporating domain and formal knowledge may beused. A feature may be represented as a semantic or semantic group. Apartially realized feature may be one that doesn't include all theassociated expected (goal) semantics and/or the factors associated donot meet a baseline interval threshold or requirement. In some examplesthe intervals are based on semantic intervals.

A semantic system doesn't require extensive training sets and in generalis more optimized for real time utilization due its capability offiltering unwanted noise and features based on the semantic model. As anexample, in feature extraction techniques some features may not beinferred if the semantic system deems them as not being realizable basedon the semantic model, semantic rules and semantic orientation.Alternatively, a partially realized feature may be inferred based on thesemantic model (e.g. based on a partially realized semantic group,semantic factoring etc.). As explained before, inferring the semanticgroups may be based on sensing, semantic attributes, localization,timing and semantic analysis. While the semantic attributes andlocalization may be associated to entire objects or features, they mayalso be associated to simpler artifacts or features like a partialcontour, shape etc. The semantic model may use semantic groups offeatures for single object or multiple objects detection. An object inan image can be recognized via the semantic attributes associated to itscomponents or features.

As part of the semantic chain development, the semantic model comprisingsemantic rules, semantic routes, semantic groups and others may evolvethrough learning.

The localizations within various semantic fields may be based onsemantic determinations wherein features, objects, signatures, groups offeatures and groups of objects are determined and correlated in variousimages, semantic scenes, semantic fields using timings (e.g. semantictimings) associated with the semantics and the semantic model.

Temporary or permanent semantics, semantic identification and/or ids maybe assigned to objects and groups. Temporary identification may be usedfor preserving privacy; the system may invalidate and/or discardtemporary identification after an interval of time; the interval of timemight be based on semantic time intervals and the system uses semanticanalysis for invalidation and/or discard. Sometimes the semanticsassociated with temporary identification may be processed and/ortransferred to the permanent identification. The information transferredmay be filtered based on semantic gate and/or access control rules forprivacy preservation; in an example, only a subset of the semanticsinferred for the temporary identification are transferred to a permanentidentification.

The system may also ensure data governance and access control to data.As such, data is stored in semantic memories and managed (e.g.invalidated, deleted) and/or accessed based on semantic access control.In further examples, a semantic wallet comprising identification,authentication and encryption keys may be used to gain (which may inaddition be viewed as a semantic gain and/or drift) access to data byallowing access at various levels in a semantic model hierarchy. It isto be understood that the semantic wallet may be also stored as ahierarchical semantic model and be encrypted based on biometrics,password, multiple factor authentication, temporary tokens and othertechnologies.

In some examples, the wallet is comprised and/or stored in a semanticmemory, optical, radio frequency and/or other electromagnetic device.Further, wallet information, identities and authentication may becommunicated via various protocols and/or further techniques some ofwhich are explained in this application.

The transfer of data between various semantic groups, endpoints, areas,regions, volumes, renderings, systems, devices, files, databases, fieldsand/or controls may be semantic gated and/or conditioned.

In some examples, the system uses semantic routing and semantic analysisto distribute documents to fluxes via semantic gating and semanticprofiles.

In further examples, documents, multimedia, files, texts, paragraphs andother ingested or processed data is associated with semantic artifactsbased on semantic inference on content and/or semantic identification.As such, the system may perform inference, reconstruction, routing andgating based on such artifacts. Further, the system may perform accesscontrol on such (ingested) artifacts and/or data by deleting (e.g.from/of artifact, from memory and/or via induced incoherent and/orcoherent collapsed conditioning etc.), scrambling (e.g. potentially viainduced crypto conditioning), obturate, obscure and/or collapse theparagraphs and/or information based on the semantic access control rulesand/or further semantic analysis. Further, the (ingested) artifacts maybe routed and/or gated within the semantic network. Further, orsubsequently, the system may perform composition, overlaying, rendering,conditioning and/or further semantic analysis of the receivedinformation in rapport with artifacts having a semantic identity (e.g.associated with a disseminated artifact, distributed artifacts,document, paragraph, object, user and/or person etc.).

Semantic rules comprise semantic composition, access control, timemanagement, ratings and factors.

Localization and distance to objects in some vision systems is achievedthrough diversity sensing using multiple vision sensors.

Vision sensors may use photodetectors arrays.

The objects, signatures, groups are correlated in various images andscenes. In an example, semantic orientation and semantic driftthresholding is used for correlation.

While in real time environments full object reconstruction andrecognition may be difficult, tracking of various artifacts and semanticfield development based only a limited number of leadership semanticartifacts, attributes and/or features, potentially comprising semanticgroups, may prove more efficient. As such, in a high velocity datascenario as a stream of data is interpreted the system may adjust basedon the environment and timing the factors of a particular sets ofsemantic attributes that identify a feature and/or object. If throughprevious semantics the system identified in the stream of images a carand identified the color red for the car and the system determines thatthere is no likelihood that another artifact of color red may appear orbe visible in the direct semantic field then the system may just detectthe location of the car by simply comparing, identifying, localizing andtracking the color red in the image or video stream (contextual leaderfeature). The system may increase the factor/weight of the red colorsemantic attribute in regard to identification of the vehicle while forexample it may gate other locations of red appearances in relation withthe car just because those locations are not feasible or unlikely to bereached by the car. The system may group such features and track thegroup of features and use any semantic grouping techniques andoperations; additionally, besides the relative position, the relativedimension of the feature is also considered. The relative positioningand relative dimension may be related to semantic artifacts, endpoints,links, semantic indexing and factoring and/or elements (e.g. sensingelements) in the network semantic model. It is to be understood thatalthough the color has been used in this example, other particularitiesand/or components may have been used to identify, speed-up and/orimprove the identification of the car in such particular contexts,locations and endpoints. In a similar example the system monitorslocations and identify objects passing through the locations andsemantic model; while identifying an object and/or type at a locationthe system may determine various other semantics (e.g. particularities)associated with the identification, object and/or type (potentially viasemantic groups). Further, the system is able then to better monitor andidentify objects at or within locations based on the knowledge ofmonitoring the movement in and out from a location or in general basedon detections at endpoints and/or network model. In a similar way thesystem may associate and/or identify objects, features and/or semanticattributes with semantic groups (e.g. groups of composite objects,features etc.) based on semantic analysis (e.g. groupings atlocations/areas, network semantic model inference etc.); thus, thesystem is able to further track particular objects in the semanticsystem based on such semantic groups. Semantic groups may be updated atany time based on further semantic inference. In an example, if thesystem detects a forklift with a color orange and orange tires itassigns such semantic attributes to the particular forklift object thatmay be tracked in the field. If later on the system detects that theforklift doesn't match the tire detection pattern and had the tireschanged with a set of black tires, then the system updates the semanticgroup (e.g. add leadership and time management to the added color oforange, while decaying and add time management rule to the change ofprevious color of black to orange) associated with the particularforklift to reflect the change in color of tires. Thus, the system isable to keep the identification on the particular forklift object withinthe semantic field based on semantic analysis even when some features orleader features change. Alternatively, or in addition, the system mayuse other observations, external semantics and/or semantic fluxes toupdate the semantic group in the previous example (e.g. receiveinformation regarding the forklift change of tires from a TIRE INSTALLERflux; CHANGED TIRES OF FORKLIFT IN THE LOADING AREA 1 TO BLACK TIRES.).

In an example, the semantic network model is mapped absolute or relativeto a car's position and/or car's coordinate system.

The system may have a reference group within the semantic model and thesystem performs relative and/or absolute comparison of the mappedsemantic field to that semantic group. The semantic group may be staticrelated to the observers (e.g. sensing, semantic unit, semantic engineview) reference coordinates. In addition, the system may performrelative inferences to the other artifacts in the field and potentiallyinfer factors and indexing. In an example, the system has a semanticgroup representing the flat bed of the composite post carrier, flat bedor hood of a car and as such the semantic inference will look to adjustthe semantic model/views comprising this semantic artifact inrelationship with the semantic scene/field development and/or mapping ofscene/field.

In an example, the system will look to find semantic path groups in themodel that may allow the passing of the hood artifact. The system mayinfer that a path and/or endpoint group comprises an artifact whichresults in deeming the path and/or endpoint group as non-feasiblebecause a denied semantic has been inferred for the artifact. It isunderstood that the paths and/or endpoint groups may be linked tohierarchies in the model. Further the system uses semantic orientation,shaping and indexing for determining the hood artifact fitting andshaping. In some examples fitting and shaping may be used to keep a postand/or vehicle in a virtual and/or physical lane.

The system may use dissatisfaction, concern and/or stress factors inassociation with fitting and/or shaping. In some examples, the systemfits and/or loads a post carrier (to storage/parking) based on semanticzoning and low concerns to fit into space. Based on further inference onthe goal achievement and/or further evidence it may adjust the concernsfactors.

Techniques such as fitting and shaping may be used to infer and optimizeartifacts (and semantic groups thereof) storage, positioning, design andtravel in particular areas and/or volumes (e.g. as mapped to semanticmodels).

The system may project goals and/or semantic budgets of fitting and/orcollapsing an artifact (e.g. endpoint, route etc.) and/or groups ofartifacts in another artifact or group of artifacts.

Semantic factors and/or budgets may be projected and/or collapsed basedon the inference in a semantic group and/or collapse of a semanticgroup.

In some examples, the system uses overlay semantic artifacts, associatedfactors and/or budgets on a semantic model and/or hierarchy to inferprojected views, semantic orientations, semantic groups, routes,budgets, factors and further semantic artifacts.

Fitting and shaping may be combined with semantic analysis on habits,purpose, uses and customs. In some examples, the system uses suchtechniques to optimize furniture arrangement in a room. In furtherexamples, the system uses such techniques to optimize storage of postsand/or containers in a garage, transportation or logistic cargo.

Semantic groups whether or not partially realized are identified andtracked by a set of factored semantic attributes.

The identification of the locations of interest in the image,represented by the objects or the semantic groups of interest are basedon semantic attributes, semantic shapes and other semantic artifacts;examples may include color, shape etc. Further, the system infersindexing comprising rate change factors and/or indicators of location,dimensionality, size, attributes, semantic routes and/or furthersemantic artifacts. Alternatively, or in addition, the system infersfactors and/or indicators associated with changes of location,dimensionality, size, attributes, semantic routes and/or furthersemantic artifacts.

The locations may be based on depth information if the image capturecomprises such information (e.g. based on TOF, stereoscopic visionindexing etc.).

The systems presented before are used in radar type applications. Thesystem uses the reflections and backscattering of the transmitted wavesfrom the illuminated objects to identify entities and infer semanticattributes related to those entities. As such, the semantic system isable to infer any type of semantics as explained above based on thelocalization and probing of entities. The entities may be detected basedon radio frequency sensor diversity, measurements, semantic analysis andadjustment. As such the semantic system may use hierarchical thresholdcalculations and semantic analysis on the received measurements,waveforms or signals to determine the location and/or semanticattributes for the detected objects.

The semantic system may store semantic inference rules, semantictemplates, patterns, signatures related with measurements, waveforms,signals. In a typical RF application, the system receives and processessensing data via analog and digital components and blocks (e.g. RF frontends). The front end may embed a semantic unit. The analog to digitalconversion is usually a bottleneck in high resolution sensing systemsand thus having a semantic engine coupled in analog and/or discretedomain may provide more efficient sensing, closer to the sensingelements (e.g. antennas) while increasing dynamic range.

In one example, the semantic engine controls electrical and opticalblocks and parameters for improved efficiency (e.g. voltage and/orcurrents, element charge).

The system may organize groups of measurements, signals and/or waveformsin semantic groups and use semantic analysis and semantic groupconditioning for semantic inference. The semantic model may comprisepatterns based on semantic groups whether group dependent or groupindependent.

When coupled with radio frequency and optical front-end systems thesemantic engine is capable of advanced semantic inference includingobject identification, localization and behavioral analysis. Such frontends and components may comprise antennas, lenses, photo elements,lasers, radiative elements, radiative meshes, beam steering meshes etc.

In some synthetic aperture and/or interferometric embodiments the returnsignals may be correlated for obtaining spectral images containing thespectral renderings of the objects in the field of view. The intensityof pixels for scanned field varies based on the reflection (e.g.backscattered waves) waveforms obtained from the illuminated artifactsand depends on the dielectric constant. The dielectric constant inmaterials and other natural or artificial artifacts increases in thepresence of moisture and as such the signal to noise ratio increases. Inanother example the signal to noise may decrease based on semantic fieldobjects' arrangements (e.g. as detected by optics/camera and/or rfsensing). As such, being able to interconnect various inferences (e.g.optical detection of rain, moisture sensor, RF/optical reflectivity)with the return signals will help with the interpretation of the returnin any type of reflection waveforms whether backscattered ortransmitted.

It is beneficial to adjust polarizations in order to achieve signaldiversity and hence improve detection.

In general, the return signal from an illuminated artifact are receivedwith the same polarization as the transmit signal.

However, in particular cases of vegetation, special materials and otherartifacts, depolarization may occur; depolarization determines thetransmit wave to be scattered and vibrate in different directions withvarious polarizations. Volume and surface scattering usually result indepolarization. For example, vegetation may be well detected through thedepolarization effect.

As such, various signals, streams, frames, images and renderings may becaptured based on various polarizations and be analyzed and fused tomore confidently detect artifacts and their characteristics based ontheir signatures in various polarizations.

Also, by varying other parameters (e.g. amplitude, phase, frequency,chirping) the scattering signature is changed and as in a similar way asthe previous example of various polarization settings the received datamay be fused to detect the artifacts in the field.

Multiband multi-polarization radar and optical systems acquire images atseveral wavelengths, polarizations using diversity techniques. Byvarying the wavelength/polarization diversity settings is feasible tocreate color images that render various surface properties in differentcolors and as such being processed accordingly using semantic analysis.Color models such as RGB (red green blue), HSV (hue-saturation-value),HSI (hue-saturation-intensity) and HSL (hue-saturation-lightness) may beused for semantic analysis, semantic augmentation and/or rendering (e.g.associate semantics with commands, voltages, currents and other controlmechanisms in order to control display elements, augmentation elementsetc.). It is to be understood that the display and augmentation elementsmay comprise any hardware and bioengineered components and blocksenumerated in sections of this disclosure.

The semantic engine may use goal-based inference for determining thebest semantic routes to follow. The goal may be based on achievingsemantics, particular semantic factors (e.g. rating) and any combinationof those; alternatively, or in addition, a goal may be based onachieving association/de-association of particular semantic artifactsand tracked artifacts and potential factors based on association.Further, a goal may be hierarchical and/or comprise semantic groupingand/or clustering (e.g. group dependent or group independent semanticmemory clustering and/or activation/deactivation). The goal may beassociated with semantic budgets. The goal may be used to determineprojected semantic views and view frames. Further, the system may usesemantic orientation to orient semantic inference toward the goal andprojected semantic views and view frames.

The system may establish a goal based on drive semantics, speculativeand/or projected inference.

Once goals are established the system performs semantic inference basedon goals and sub-goals. Sometimes the system uses different semanticview frames for performing the goal-based inference. The system performsinference that builds semantic routes and assesses the factors ofsemantics in rapport with the goal's factors and semantic budgets. In anexample, the system sets up a goal to gain knowledge or learn carrepair. The system evaluates based on semantic analysis that learningabout a car's engine provide the most rewarding goal outcome (e.g. “easyto understand” factor based on projection inference of existing modelsapplied to information about engine, “higher pay” factor based onprojection and so on) and, as such, establishes learning routes anddrive semantics that include semantics associated with the engine.However, as the system uses the semantic route to perform inference itmay infer that learning about the engines sensor suite is less riskywith similar rewards and as such it may change the sub-goal and/orpriority to learning about car's sensor system including CAN bus, OBDinterface etc. As such, the system updates the semantic route to adaptto the new sub-goal. The system assesses and/or change the goals and subgoals based on semantic artifacts, other semantic factors, externaland/or user feedback etc. In a related example, the system may learnfirst about the engine sensor suite and further determines that asub-goal for learning about engine's injection or other components (e.g.transmission) may be more rewarding based on the semantic view ofoperation. The system may use semantic orientation to determine thesemantic drift between the pursued semantic routes and the updatedsemantic routes of the views, view frames, model etc. Thus, the systemmay assess whether the pursued routes need to be updated and adaptedbased on the updated goals, sub-goals and projected semantic views andview frames.

If the pursuing and/or projection of (strategic) goals results indecayed budgets, factors and/or further blocked inferences the systemmay decay and/or stop altogether (the pursuance of) the goals.

Sub-goals may be inferred and/or related with increasing/decreasingfactorizations and/or budgets. In an example, the system infers based onprojected analysis that the budgets are too decayed (e.g. and furtherinfer lacks of resources and/or needs—“need higher budget”, “need togain 100”) and not allowing to achieve the strategic goal of “learn carrepair” and/or further sub-goals of “learn about sensor suite”; thus,the system may perform inference and augmentation towards sub-goals suchincreasing budgets and/or satisfying short term needs which may furtherroute the inference to attaining semantics, collaborators, fluxes and/orgroups which allows higher factorization of budgets. It is to beobserved that in some cases the semantic drift between the short termgoals and long term (e.g. strategic) goals may increase (e.g. byfactorization, indexing etc.) and the semantic drift between the meansof achieving the longer term goals and the short term goals may changeas well.

Further, the goals/sub-goals, semantic hierarchy, orientation and/orrouting comprise variable (allowable) drifts and confusion. The systemmay re-allocate more resources (e.g. budgets, semantic units) tosemantic views associated with (projected) high consequences (e.g. highfactorizations) and/or risk; alternatively, or in addition, the systemmay allocate more resources to (projected) inferences which may not meetfactors, budgets (e.g. (semantic) time (quanta) budgets), coherence,confusion and/or drifts; further, the system may use alternate and/orhierarchical routing and/or gating. In an example, of an activity ofSURGERY and further ACTUATING SCISSORS may require a lower semanticdrift based on the risk factorization of projections and/orconsequences. In some examples, the consequences may be related withleadership projections, risks, diffusion and/or tunneling throughsemantic gating and/or access control. The system may re-allocateresources to such critical operations however, if the inference and/oractuation has drifted and/or is incoherent (e.g. due to decayed budgets,high drift, confusion etc.) the system may re-allocate resources(potentially to a different level of hierarchy) for finding alternateways and/or zones to employ cutting and/or scissors capability (e.g.CUTTING EB SHAPE-2, CUTTING EC SHAPE-3 (instead) of CUTTING ENDPOINTZONE A SHAPE-1). Thus, the system may reallocate resources based onsemantic factors, budgets, time management, drifts, coherence,confusion, rules and/or further semantic artifacts.

The resource allocation/reallocation may be based on short term goalsand/or long/longer term goals. Further, the reallocation may behierarchical with the short-term goals being allocated/reallocated at alower level and/or shorter-term memory while the long-term goals may beallocated/reallocated at a higher level and/or longer term memory.

The resource allocation/reallocation may be based on DNA replicationand/or remapping.

The semantic orientation provides sentiment analysis based on semanticdrifts, decaying and further factor inference. The system further usessemantic orientation and drifts to adjust projected views, view framesand further to guide the semantic inference. In some examples the systemuses the drifts (e.g. semantic drift trajectory based on pattern overlayand/or indexing) to smoothen the semantic routes and/or trajectories.Smoothing of routes and trajectories may be used for optimized commandand control, prediction, correlation, covariance, conditioning and soforth.

The smoothing may be associated and/or be used to model/implementhysteresis in some examples.

The hysteresis is modeled and/or implemented based on semantic profiles,semantic rules, decaying, drift, factors, goals, projections,intentions, desires and/or further semantic analysis. In some examples,the output of the battery unit and/or controlvoltages/currents/electromagnetic effects in the semantic post isincreased and/or decreased based on an inferred intention and/or desire(e.g. of a control unit and/or user) and further time management rules.Analogously the electrical control values of HVAC units may becontrolled in similar ways. In further examples, vehicle acceleration iscontrolled by varying electrical voltages, currents and/or magneticproperties/fluxes based on semantic hysteresis.

The system may learn semantic indexing and/or hysteresis associated withsemantic identities and store it in semantic profiles. In some examples,the system associates inferred artifacts in semantic views with drivercommands (e.g. as captured by sensors, devices and/or semantic fluxes).Thus, the system may know through semantic inference (e.g. semanticgroup, time management etc.) that the user is associated and/or actuatesthe acceleration and/or steering; as such, the system groups thesemantic artifacts (e.g. semantic routes/trails etc.) inferred from suchactuation related sensors with the semantic artifacts inferred fromfurther semantic field (e.g. environment); in circumstances where thesystem infers less used and/or weighted routes, high factors (e.g. risk,alertness etc.) and/or unusual/unfamiliar behavior in the semantic fieldthe system may learn rules associated with indexing and/or hysteresisinferred based on users actuation commands and/or further consequencesas further inferred on the semantic field.

As specified before the system is able to infer factors (e.g. rating,weighting etc.) for a semantic; the system may use factor plans.

The factors may be used to determine commands to the controlledentities/components/blocks/devices including actuators, sensors, I/Oand/or transducers. The commands may be linked and/or specified withsemantics; alternatively, or in addition the commands can be specifiedand/or linked with a parameter and/or value to be applied to thecontrolled artifact; in an example, a voltage or current interval may bespecified for a specific command linked to a parameter. Alternatively,or in addition, the system comprises/infers and/or receives (e.g. fromuser, semantic flux etc.) a reference voltage, value/s, interval/sand/or signals which is/are pondered/correlated/convoluted with acorresponding factor (e.g. weight) from a semantic. In another example,the voltage and current are indexed in time based on semantics andfactors (e.g. indexing factor). The system comprises an indexing factorthat occurs with each semantic and is applied to the current value. Theindexing factor may be positive or negative.

The command may be a function of the factor of the semantic associatedwith the command. As an example, the value of a parameter or voltage maybe a function of a weight.

In some examples the system uses semantic routes to implement commands.

A command may be represented as a semantic. The semantic may be acomposition linked to a semantic route and/or group of other semanticswhich may be associated with commands; as such the semantic commandchain is executing based on associated compositional semantics and/orgoals possibly based on timing, factors, orientation drifts and/orbudgets. The factors of the composite control semantic and itscomponents are calculated based on inference that may include thefactors of the entire compositional chain of the command execution. Inan example, the factors of a composite semantic may be a function of thefactors through the compositional chain, groups and/or routing; as such,all the semantics of the compositional chain are contributing to thecommand through the factors associated with them. The factors may beused to issue commands (e.g. voltage, current, signal, digital commandsetc.). In an example, the semantic engine infers a semantic with aspecific weight and based on the semantic model which may include acompositional template (e.g. comprising semantic groups and/or routewherein the semantic defines, belongs or drives semantic coupled termse.g. synonyms) and possibly factors rules associated with the semantic,the system infers factors and budgets for the compositional templatesemantics (e.g. semantic group/semantics); the compositional semanticsmay be associated with actions and commands and as such the actions andcommands are pondered with the inferred factors for the compositionalsemantics (e.g. for a command control an associated voltage is adjustedbased on the factor inferred for the compositional semantic associatedwith the command control). Further, the compositional semanticweights/factors (e.g. semantic route semantic weights/factors) may beadjusted based on the composite semantic weights/factors. If the entity(e.g. IO component, sensor) associated with the command is unable toperform the associated command and/or route in a desired budget thesystem may not issue the control command to the particular entity; itmay infer other semantics, or possible expand or adjust the initialsemantic (e.g. through semantic route expansion, semantic orientation,drift etc.) to compositional semantics, infer/determine new semanticroutes further until the system infer/determines that the overallsematic objective or projection is achievable as per goal (e.g. budget).

Additionally, the system may receive feedback from the command controland adjusts the semantic model including the weights/factors, rules,templates and other artifacts based on signal feedback (e.g. fromsensors that perceive the effects of the commands).

In one example, the system performs inference on a composite semanticuntil achieves a particular factor/weight, potentially within a budget;subsequently of achieving the goal it may expand the semantic usingother semantic routes and inference paths; alternatively, once the goalis achieved the system doesn't use that semantic for further inferenceif the semantic is decayed in the semantic view frame.

In further examples, the system speculates at least one semanticartifact and compose it in at least one view and/or hierarchical leveland further assess the coherency of narratives. It is to be understoodthat the speculative artifact may be based on situational and contextualunderstanding based on semantic artifacts at a higher abstraction and/orhierarchical layer/level. Further, such abstraction or hierarchicalunderstanding may be controlled through access controls, authenticationand data governance.

A semantic system may establish semantic routes through goal-basedinferences. The goals may be associated with semantics and used to inferor determine a set of semantic routes and semantic budgets which thenmay be pursued in order to achieve the goal; this may include executingcommands and continuously updating the model based on sensing andfeedback. When the system achieves the goal (e.g. infers or reinforces asemantic and/or achieves a factor value/interval for it) it rates theexperience and the system adjusts indicators and semantic factors (e.g.costs and/or risks).

The semantic engine couple's information from a variety of sources.

The system may use and infer semantics from databases, text, web pages,files, spreadsheets, visual and non-visual environment and so on. In oneexample, semantic agents or units are actively monitoring such datasources and connect through the semantic infrastructure in a distributedmanner.

The systems maintain semantic artifacts associated with entities whereinthe semantics are representative of the capabilities or functionality ofthe entities and are potentially acquired when the entities register orare detected by the system.

In an example, an automobile ECU determines a set of particularsemantics related to a semantic route and sends the semantics to sensorsand actuators sensors by matching the particular semantics with theassociated capabilities or functionality of the sensors. The receivingentities may receive the semantics via semantic fluxes, potentially withassociated weights/factors and perform semantic analysis includingcomposition, routing, and/or orientation and make their own decisionswhether to execute actions or not. The semantics may be broadcasted, andthe sensors may listen to all or particular semantics based on semanticview, semantic view frame and/or semantic route. The semantic view andsemantic view frames may be particularized for each entity as explainedin this application. Further the sensors may be mapped to a semanticnetwork model.

The ECU may send semantic routes and semantic budgets to sensors. Thesensors may use the route selectively wherein the sensor determinescommands associated with semantics of its own capabilities (e.g.registered or marked semantics) in the semantic route and potentiallyexecute them within a required budget; further, it listens for othercommands that are completed by other entities until the semantic routecompletes.

At any given time, the semantic sensor may consider multiple routes atmultiple levels based on execution, sensed context and/or orientation.As mentioned, the semantic sensor performs semantic analysis on its own.

The system may detect eavesdropping and malicious information injectionattempts wherein the system infers high incoherency, confusion, driftand entropy (of) factors.

In an example the semantic orientation and semantic drifts aredetermined and associated based on analysis involving synonymy and/orantonymy. The system calculates the shift/drift from goals andprojections based on composition and factorization of semantics inroutes, view frames and views in rapport with a goal. Thus, the systemmay highly semantic factorize synonyms and/or antonyms of the goalsemantic when performing semantic analysis.

As such the system is able to correlate the information from a multidomain, multi-source and heterogenous environments, perform sentimentanalysis and learn.

The system may determine a factor/weight for a semantic in a particularcontext (e.g. semantic view frame). In one example, the factor/weightmay be associated with a sentiment of suitability of the semantic in theparticular semantic view. In another example, the system executes anaction (e.g. for a car automation application it controls an analog ordigital interface to decrease the speed) based on an inferred semantic;the system may have coupled the action in the semantic with and at leastone expected semantic in a semantic route to occur (potentially within asemantic budget) while or after the action semantic is executed; thus,while executing the action or shortly thereafter the system correlatesany inferred semantics with the expected or projected semanticsartifacts; as such, if the system doesn't infer the expected semanticand/or factor, the system may further adjust the semantic route, modeland potentially the weights/factors of the semantic route, rule or linkrelated to the action semantic and the projected semantic; in such anexample, the system may infer a semantic and/or factor that reflect apositive or negative sentiment and is used for characterizing the bondbetween the first (e.g. action) semantic and the second (e.g. projected)semantic in the semantic route; if the bond needs to be tuned, theweights/factors are tuned and/or a sentiment semantic is associated withthe first and second semantic and/or semantic route while potentiallyforming a semantic group. In an example, a negative factor may representa negative sentiment in rapport with a semantic artifact and/orindicator. Positive and negative sentiments may be represented as afactor associated to semantic artifacts and compositions of semanticroutes, views, trails and/or view frames; as such, the system composesthe semantic factors and other performance indicators based on thesemantics associated to trail, route and/or view frame and theircomponents; sometimes it may be based on the outcome of expanding asemantic route into a semantic view frame. The signals and/or commandsassociated with a semantic artifact may be conditioned and possiblyassigned new semantics and semantic factors (e.g. if only a part of theaction was having a positive sentiment, the system may gate the actionto a positive, negative and neutral sentiment and/or signal) and furtherassociate those with the semantic model, semantic routes and semanticrules. Positive and negative sentiments may be in rapport with asemantic route or shape selected for the context or semantic view. Thepositive and negative sentiment may be used and/or inferred based onsemantic orientation.

A semantic model may be expressed via any methods which convey languageincluding text, speech, signs, gestures or any other interface. Thesemantics may be conveyed through localization of artifacts within asemantic field and semantic inference based on semantic model. Whenconveyed via such an interface the system converts the ingested datainto a temporary meaning representation and then compares the internalmeaning representation with the temporary representation. Sometimes inorder to speed up the process, the system doesn't fuse the internalsemantic model with the newly processed meaning representation at thetime of the configuration; the process may be delayed or allowed basedon semantic inference and analysis including time management. In anexample, the previous configuration is stored as text and a differencein meaning representation with the newly configured text is computed viaa meaning representation interpreter and then the difference is appliedto the semantic model configuration. This may be more efficient thatapplying the whole received configuration to the semantic model; theinterpreter may be run on a separate processing unit for efficiency.

Consecutive configurations may be fusion-ed together for moreefficiency; the configuration fusion may occur at the lexical level(e.g. text concatenation) and/or at the meaning representation level.

The semantic system uses the semantic composition to infer semanticsfrom the sensor subsystem; semantics may be associated with elements ofa specialized or more general vocabulary and/or language. Further, thesystem may perform semantic gating on configurations.

In some example, the user specifies groups of synonyms, antonyms andother semantics that are related with a semantic. The elements in groupsare by themselves related with the original semantic through semanticattributes and/or semantic groups which represent a semanticrelationship in a general or particular context. In one example ofgeneral context the semantic attribute might be SYNONIM, ANTONIM etc. Inanother more particular example, the semantic attribute might be relatedwith particular contexts, representations and/or semantic artifacts.

The semantic expiration or semantic route collapse may mean that thesemantic network graph, mesh and/or semantic memory are adjusted basedon inference.

In semantic expiration, the semantics may expire once the system infersother semantics; that might happen due generalization, invalidation,superseding, decaying, time elapse or any other inferences duringsemantic analysis. These processes are implemented through theinterpretation of the semantic rules, semantic routes and semantic modelby the semantic engine.

The semantic routes represent a collection of semantics and/orsynchronization times that need to occur in order for a system to followa goal and/or infer particular semantics. As such, the semantic routesare very suitable for context based semantic inference, planning and forensuring the system's reliability and security.

The inputs may be interpreted and validated based on semantic inferenceincluding semantic routes and semantic analysis. In one example, thesystem may calculate correlation or covariance factors betweentrajectories, signals/data (unconditioned, conditioned, semantic waveetc.) of semantic routes and/or an environment signals/data. Thecorrelation/covariance factor may be used to select the best semanticroute for interpretation and validation of context. Thecorrelation/covariance factor may be compared and selected based on athreshold and/or interval (e.g. semantic factor, drift based). Thecorrelation/covariance factor may be based on all the semantics thatmake up an environment including semantic view and/or semantic viewframe and are within the system's semantic coverage; thecorrelation/covariance factors may be calculated using all or onlyselected semantics (e.g. leadership) in a semantic route and determineand/or be associated with weights/factors for the inferred semantics.

Further, the correlation factors may be used in semantic orientation(e.g. for comparison, drifts etc.).

Correlation and covariance inference and/or factors may determinefurther inference of covariances, causality relationships and/orfactors.

The semantic routes may be also associated with the semantic rules (timemanagement, access control, rating, weighting etc.) for providingadditional granularity and control.

The synchronization times and time intervals as specified in thisapplication may be based on semantic time.

The correct identification of the categories of features and objects inthe semantic field might prove useful in controlling the parameters ofthe sensing devices, orientation, field of view, sample rates, filters,timing, weights/factors of various modalities and others. In someexamples the shape recognition is used in biometrics (e.g. imagingfacial recognition, fingerprint, electromagnetic body print and/orsignature etc.).

The sensors may register their semantic capabilities (e.g. optical,visual), identification and mission and the system uses semanticinference based on these characteristics.

Global navigation satellite (e.g. GNSS) sensors may be used to map thelocation of objects; the location of objects can be also identified viavision, thermal, RF and other radiation energy backscattering sensing.

This location data may be fused to identify the location of artifactsand objects in a particular area. In the case of an autonomous vehicle,various sensors may sense the surroundings and determine the best linksand paths to follow based on various factors and semantics.

The GNSS and other location data can be compared for artifactidentification and positioning.

The locations in images or videos may be mapped to locations in thesemantic model based on depth, distance and the relative positioning andfield of view of the sensors that captured the images and videos.

A semantic engine may use general coordinates or relative coordinatesfor its semantic network models. The general coordinates are associatedwith a central model and a centralized coordinate system wherein thesemantic system may have a full or particular view. The relativecoordinates are associated with a localized model and a localizedcoordinate system (e.g. relative to an observer and/or a semantic group)wherein a semantic engine may have a full or particular view.

In some examples, the system uses both coordinates systems wherein thesystem maps the localized model to the centralized model. In a similarfashion the system may map stationary endpoints (e.g. semanticstationary) to a dynamic environment.

In an example of a semantic post and/or self-driving vehicle thesemantic model may be determined relative to those and/or observers(e.g. optical or radar sensor in the dashboard). In an example, avehicle's hood represents a semantic stationary group of endpoints whilethe semantic field comprising other semantic artifacts develop in adynamic way.

The general global positioning coordinates, including that of the car,may be known via global positioning sensors and calculations relative toknown coordinates.

As such, the car itself may represent the reference positioning inregard to its sensors and the semantic model that maps and containslocations around the car.

A reference positioning can be detected via global positioning includingglobal navigation satellite systems. Alternatively, or in addition, thecoordinates may be provided via infrastructure. As an example, thesemantic system may receive the position from a wireless infrastructureand/or mesh; alternatively, or in addition it may sense a sensor and/orobject positioned at a certain location. Further, the localization maybe enhanced with inertial navigation sensing.

The semantic model locations may be dependent or independent of therelative position of the car and are used to determine the feasiblelinks and paths to travel based on semantics. The system may use acombination between the two coordinate systems.

As specified before a semantic attribute may be detected through opticaland RF means and be linked to a location. Such detection and/orcommunication which may use various adaptable modulation techniques inanalog and/or digital domain (e.g. amplitude, frequency, phase and anyvariants and combinations) on one or multiple fluxes.

The semantic system may use such semantic artifacts and routes tointerpret access control rules in the semantic field which assesses thelinks, paths and routes that should be followed or should not befollowed. The semantic system then infers and determine semanticattributes based on the links and paths of travel to be followed.Inferred, pre-determined or predefined semantic routes may also be usedto determine the optimal or mandatory links and paths to follow based onthe semantic attributes in the routes and eventually the order andtiming of those.

The transferring of data within the system may include establishingsessions and/or channels between any number of components (e.g. RFcomponents); sometimes sessions establishment and/or management involvesthe management and association of semantic groups of components.Sessions between semantic group of components may be formed usingsemantic techniques; an important aspect is the system's cybersecurityand as such authentication mechanisms (e.g. certificate, code,signature, challenge response) may be employed. In addition, challengeresponse may be used to infer/determine/identify semantics and provideaugmentation on the particular challenge (e.g. question-response based).Challenge response techniques may involve certificate, key and signatureauthentication. Sometimes multi stack protocol systems rely on thehigher levels of the protocol stack implementation for data encryptionand as such the lower level channels are not encrypted. Alternatively,the hierarchical stack encrypts the data at each level. The hierarchymay be represented as a semantic network graph. The encryption type maybe inferred/determined on semantic artifacts and comprise semanticgroups of elements, connections, sources, destinations, memory, blocks,data etc. Some systems separate the traffic into control and trafficplanes wherein traffic plane tunnels network traffic through specifictransport and tunneling protocols. The QoS (quality of service) inmultiple tunneling connections is difficult to assess; semanticinference techniques including budgeting, quantification andfactorization as explained in this application may be used for enhancedQoS protocols.

Collaborative systems (e.g. posts, vehicles) implement point to point,implement vehicle to vehicle communication in order to coordinate thepath of travel that they pursue and for avoiding collisions. While thecommunication may happen in real time allowing the vehicles tocoordinate the trajectories, sometime the systems are unable tocommunicate due to various factors including communication or networkunavailability. In such cases the vehicles semantic units woulddetermine the best trajectory to follow without collaborativeinformation; the determination may use various inferences and/orassumptions regarding the vehicles and objects as detected in thesurroundings (e.g. based on identification, semantic groups, trajectory,behavior, intentions, entropy etc.).

In general, for vehicle to vehicle communication to be effective thesystems should reference the semantic fields to a set of commonly knowncoordinates and locations. Those locations may be general/global or canbe localized in the case of using other localization techniques orrelative system of coordinates (e.g. relative to the car itself, whereinparts of the car are considered the reference point as explained aboverelative to posts, sensors etc.).

Groups of systems (e.g. based on semantic groups) may form a meshnetwork for communication and localization using the RF elementsgroupings. The mesh network may be temporary based on location and bemanaged based on semantic grouping (e.g. time based, location basedetc.). The mesh network may use any spectra in the electromagneticdomain wherein the coordination may be based on semantic inference andanalysis.

Vehicle to vehicle and vehicle to infrastructure communication help thereal time semantic systems of the vehicle to develop and update theirsemantic models. For example, if two cars A and B are in communicationand car A transmits to car B that the road in location L IS MODERATELYSLIPPERY just because an accelerometer sensor detected that the wheelsLOST GRIP I sec, then the car B semantic system will adjust its semanticmodel that is related to SLIPPERY semantic and location L with asemantic factor corresponding to a moderate condition. Further, theSLIPPERY may be sent through the mesh potentially with associatedfactors and expiration times.

The semantics based on acceleration and orientation data may be used invehicles electronic stability control by actuating various suspension,traction and braking components; such information may be provided onmultiple axes by accelerometers and gyroscopes.

The system may infer that certain locations in the semantic model arenot feasible to follow at certain times due to the potential lateral orforward acceleration produced and potentially other hazardousenvironmental and road conditions that may determine the vehicle to losestability (e.g. ROLLOVER HAZARD) or grip; as such, the semanticinference will ensure that safe and feasible paths are followed invarious road conditions. Sometimes that decision is made at thesensor/actuator level where the sensor/actuator has a limited intervalof action on semantic inference possibly controlled by semantic rules(e.g. access control, time management).

As such, the semantic model and access control rules in an autonomousvehicle semantic system are dependent and adjusted, based on factorsincluding road and environmental conditions, vehicle stability sensorsand controls, vehicle to vehicle communication and other internal orexternal factors.

The sensors or semantic units may register their capabilities (e.g.modeled through semantic attributes) to a memory and/or communicate themthrough semantic fluxes and/or semantic waves.

In pub no 20140375430A1 semantic identification and marking has beenintroduced.

Semantic marking may be used for identifying the semantic rules and datato be retained by a computing or semantic unit in a distributed semanticinference system wherein the system retains the rules for the markedsemantics and ignores and/or discard the rest.

Semantic identification commands can be issued to groups of elements andthe elements identify themselves with a semantic artifact (e.g.semantic, semantic group); sometimes the identification is achievedthrough semantic analysis. The system may issue a speculative semanticidentification command and a semantic unit/element may need to speculatewhether it can factorize, infer and/or perform the semantic within thebudget and based on the assessment identifies itself as part of thesemantic group or not.

The speculative inference process and semantic artifacts may beassociated with indicators and factors for assessing potential successand failure (e.g. risk factor).

The computer and/or processing hardware may comprise chains of semanticunits that perform parallel and/or serial inference. It is understoodthat the semantic units may be connected through any interconnecttechnologies including electrical, optical, electromagnetic and anycombination of those. While the system may use semantic modulation andsemantic waving for semantic units communication it is to be understoodthat alternatively, or in addition, they may use any existing protocols(e.g. embedded such as SPI, I2C, network and/or wireless,serializer/deserializer, peripheral component interconnect buses etc.)to encapsulate and/or modulate semantic flux information and/or semanticwaves with semantic analysis.

Sometimes they process the information in a highly distributed semanticfashion.

Semantic identification and/or semantic marking may comprise all thetechniques used for collaborative semantic routing, gating, shapingand/or inference. Further they are applicable to all the semanticartifacts, semantic model artifacts and/or semantic rules.

As such, processing units, or groups of processing units maycollaborate, perform semantic inference and redistribute the semanticinference artifacts and semantic model among themselves. The computerperforms semantic inference and potentially stores the paths and/or theaddress/identification of the units that were targeted and/or used forprocessing goal-based inferences and/or for inferring a particularsemantic or theme. Once a semantic or theme is inferred a semantic unitmay use semantic analysis and determine that other semantics may beserved in a particular way by such a semantic inference grid route andas such sends a semantic marking command to the semantic grid route witha particular semantic to be memorized by the semantic units andpotentially link it with the semantic inference rules and with thesource semantic unit and/or group.

Semantic models and inference rules are sent to the semantic unitsand/or groups and the semantic units select only the semantic artifactsand/or inference rules associated with the semantics that they inferredand/or are marked for and store them in the memory; in an example thesystem uses composite semantics between inferred semantics and markedsemantics. As such, the information is distributed optimally based oneach processing unit needs.

The semantics may be stored in the semantic/processing units inassociative and/or semantic memory. The semantics may be stored in acentralized fashion in a shared memory, in a semi-centralized fashionwhere parts of memory are distributed, and parts of memory arecentralized or totally distributed fashion where each unit stores itsown memory.

The memory and inference power may be distributed among the units,concentrators, computers, computer banks and so forth.

The semantic marking commands, semantic identification commands andsemantic rule commands use time management for optimal use of resources.As such, the semantic system may perform the markings, identification,rule and model changes and updates as specified by time managementrules.

The system senses conditions with less semantic inference activity (e.g.potentially using gate published semantic budgets) and initiates furthersemantic analysis, inference and updates on the stored data and performsthe markings and/or updates. The initiation can occur at any unit; theinitiation may be based potentially on speculative inferences, externalinput, access control and/or semantic time management rule. Theinitiation may occur also when there is an instability in the system asdetected by semantic inference and indicators; in other examples thesemantic inference chain was interrupted or broken at some processingunit and as such was unable to process or transfer the semanticinformation to the other units; in another example semantic budgets arenot balanced (e.g. composed voltages V related with particular semanticartifacts are high) in a potential endpoint localized, semantic orsemantic group manner. Sometimes the transfer of the semanticinformation between semantic units may be purely related with memoryoperations (e.g. changing addresses, pointers, links, copy, storedweights/factors, structures, clustering and such), DNA replicationand/or remapping.

In order to improve sensor fusion, the system may use semantic fieldprofiles wherein the semantic field profile is based on theparticularities of the semantic field in a particular area, at aparticular time or in a particular context. The semantic field profilesmay determine the priority or enablement of the sensing capabilitiesthat are being used and the fusion factor of each modality. For example,during night an infrared sensor or heat vision camera may be given morepriority than a regular vision or imaging sensor. As hence, timemanagement rules coupled with semantic inference on sensing,capabilities and attributes establish the factors of the sensingcapabilities and particular sensors. Also, a time management rule may beused to bias factors of particular semantics, semantic groupsdeterminations and other semantic artifacts. As such, when a timemanagement rule enters into effect based on time, semantic and/orinterval determinations, a factor may be assigned and/or indexed for aparticular semantic group that can be used in semantic sceneinterpretation and development. For example, in an urban area, asemantic group representing groups of people may bear leadership in thesemantic scene. Additionally, the higher weight may be also based onsemantic principles that specify that a particular semantic or semanticgroup bear leadership in particular categories of semantic sceneinterpretation, detection, development and action. Additionally, basedon location and other factors the system may decide which features orsub-features of the object leaders. For example, in a relatively closeproximity the detected facial features might be preferred over otherfeatures such as height, width or dynamic features as walk, clothes etc.There may be always features that have leadership (e.g. high factor) insemantic determinations and they may include category, color etc.

The semantic system uses the semantic model including semanticattributes to identify objects. As the sensing conditions change thesemantic system may adjusts the weights/factors of the semanticattributes or features for semantic scene or object recognitioninference, potentially adjusting them based on factoring rules andplans. In an example, if the system is inferring that a car is presentin the semantic scene just because it tracks a semantic attribute ofcolor red associated to the car then the system may adjust theidentification of a car based on COLOR AT NIGHT factor when the colorcannot be sensed well and instead other attributes are assigned moreleadership. As such a weight is based on the sensors data, semantic timeand semantic analysis.

The system may be in a steady semantic view at a hierarchical level. Forexample, a smart post may have determined that following the lane, orpost in front is required for the time being and hence the dynamicsemantic “follow the lane” or “follow the lead” is continuously inferredat the particular hierarchy level, potentially with associated factors.

The system may have inferred a route for “FOLLOW THE MARKS” and thesystem uses the mappings of the marks in endpoints to route, determinethe path and provide actuation based on path inference. In addition,semantic factors may be used to perform actuation and commands tosteering.

In a further example the system detects DRIFT LEFT and as such thesystem calculates a composite semantic factor associated with FOLLOW THELANE and DRIFT LEFT which may be STEER RIGHT with the calculatedcomposite factor. In a further example the system infers factors,potentially on a combination of semantic network model, semanticcomposition, semantic orientation, semantic drift.

The system may have been using FOLLOW THE LANE semantic comprising aroute of SPLIT ROAD, LINE MARKS LEFT, LINE MARKS RIGHT and PARALLEL LINEMARKS potentially mapped to the semantic network model. Once one of thesemantics in the route disappear the system may readjust the semanticroute and/or composable semantics of FOLLOW THE LANE (e.g. use and/orincrease the leadership associated to mappings and groups to otherobjects, cars and landmarks). The system may use a combination ofsemantic routes for inference and to preserve the semantic views (e.g.current and/or projected) and/or goals and adjust those based onsemantic analysis.

The semantic view at each level can change based on several factorsincluding semantic analysis on signals, data, semantics whether ingestedfrom external, internal or inter-hierarchy sources and/or fluxes. Forinstance, the system may need to assess the potential semantic routesand paths that needs to be followed while preserving the semantic viewat a particular hierarchy level.

As specified in the previous example a post semantic unit might be inthe steady semantic view of “FOLLOW THE LANE” at a particularhierarchical level, however if in the semantic scene is determined thatin location L (e.g. 20 yards) a semantic group associated with personhas been detected (e.g. PERSON HAZARD ALERT) then the system infers theimpact within the hierarchical layers of semantic view based on semanticanalysis and semantic gating. For example, a location L1 at the left ofthe person semantic group formation may be determined as feasible basedon semantic orientation inference and/or speculative semantic viewdetermination and hence the system infers the semantic of “CHANGE LANE”to location L1 which translates in further sensor control and actuationcommands. Speculative semantic view determination is based on a goalbased semantic analysis as described throughout the application.

It is understood that the system may comprise more complex compositeorientation and drive semantics, routes and semantic views (e.g.includes additional artifacts for FOLLOW THE LEAD ONLY IF FOLLOWS THELANE AND DRIVES SAFE) and as such the system performs projectedinference on leader behavior, intentions, orientation and goals whilepotentially decaying or expiring FOLLOW THE LEAD related artifacts ifcurrent and/or projected semantic views indicate a negative sentiment inregards to LEADER, FOLLOW THE LANE, DRIVE SAFE and/or further safetygoals and routes. The negative sentiment in relation to such safetyrelated semantic artifacts may be associated with increasing/increasedrisk and hazard related factors, decaying and/or negative trust factorsand indicators associated to LEADER, FOLLOW THE LANE and/or DRIVE SAFE.Analogously, positive sentiments may be associated with decaying and/ornegative risk and hazard factors and further, increasing/increased trustfactors and indicators. It is to be understood that the system may usesuch associations of semantic artifacts and sentiments to learn and/orreinforce new semantic groups, rules, trails and/or routes. For example,it may reinforce a risk factor associated with a semantic group or routeof CAR, FLAT TIRE and even further risk for CAR, FLAT TIRE, ONE-WHEELER.

The system may form guiding lanes and/or routes by controlling posts,objects, devices, sensing and/or control elements. In some examples, thesystem lights up LED lights embedded in a surface in order to guidecrowds, vehicles, airplanes and so forth. Further, the width of suchlanes may be inferred based on traffic flow analysis. The system may bechallenged and/or infer goals of traffic simulation and thus performingtraffic flow analysis.

In further examples, the traffic flow analysis encompassesarrival/departure docks, gates and/or lanes modeled within the semanticnetwork model.

Semantic systems add a level of security beyond programming and/or datadriven systems. This is due the fact that semantic systems allowreducing the semantic gap and hence are more semantically complete. Areduced attack surface is ensured by the interaction via semantic fluxeswhich exposes a reduced number of entry points into the system bypotentially multiplexing them to a protocol channel and/or port. Thoseentry points can be more readily controlled and managed via strongauthentication, encryption, virtual private networks etc.; a semanticsystem can also use semantic inference to detect possible attempts toinfluence and/or compromise the system by crafted semantic exchanges.Semantic systems may detect communication channel and/or wave floodingand/jamming based on repeatability, incoherence and/or confusion factorswhich may be gated and/or used for gating within the hierarchy; further,such attempts may be isolated at particular hierarchical levels (e.g.low levels) with particular semantic artifacts based on channels and/orwave inference being gated based on particular DNA (signatures),semantic identities, thresholds intervals and/or levels. In someexamples, in order to overcome such attacks and/or challenges the systemmay use DNA replication and/or remapping at/of the affected endpointsand/or areas.

For example, inducting false semantic artifacts into a collaboratingsemantic flux/stream. Therefore, there should be ways to detect suchattempts and eventually detect, retaliate and disable attacking cybersystems. The retaliatory and disablement measures may be necessary ifthe attacking cyber systems use denial of service attacks to bring downcommunication between systems and infrastructure. However, there shouldbe careful considerations and assessment when choosing retaliatoryattack targets as many of these targets may be legitimate systemsinfected or controlled by malware.

Collaborative defenses encompassing various emission, waves and/ornetwork techniques (e.g. jamming, distributed denial of service etc.)may disable attackers and restore the communications. As such, semanticsystems may organize in packs in which semantic systems groups observeand disable a particular group for a period of time. If groups/packsconsist of semantic systems with similar signatures (e.g. based onrules, routes, model, artifact mapping inference etc.) they may takesimilar actions and therefore the pack formation is more natural towardssemantic action intensity without necessity of system interconnection.Alternatively, to increase the semantic spread, a semantic group packmay be comprised from units that have different signatures. While theattacker may try to infect some systems, the semantic cyber componentsor collaborative systems behavior semantic analysis may detect andassess intrusions. It is to be understood that the attack and/orinfection may comprise physical and/or cyber corruption and/ordisablement of systems (e.g. in case of optical sensors may includelaser attacks, or breaking lenses, obturation attacks and so on). If asemantic system is deemed as compromised the semantic system network mayreorganize and assess the factors of the semantic determinations by thecompromised system. As such, the semantic fluxes, semantics and themesfrom the compromised system may be assigned appropriate factors (e.g.low weight, high risk, hazard etc.); additionally, the semanticexchanges from compromised systems may be fed into a different cybermodel and cyber inferences be build based on that behavior knowledgeinferred by healthy systems whether collaborative or not. The healthysystem may use the cyber model and determinations for profiling tacticsor counter measures. One profiling tactic may be to acknowledge andcontinue semantic exchanges with the compromised systems while feedingthe information to the semantic cyber model; the system may create anactor or acting semantic view to cope which such profiling. Anotherprofiling tactic is to appear to accept the intruder's changes tosemantic models by creating a copy of the semantic model and keeping thelegitimate copy safe, potentially running on a separate unit; further,based on the malicious model and the cyber model creating a threat modelfor the malicious attack based on semantic analysis including semanticorientation, learning, gating and/or fusion between the two models. Thedetermination that a semantic model changes are being malicious can bedone based on various semantic factors and semantics on cyber andcommunications models.

The semantic engine may organize entities including semantic units,sematic fluxes and other semantic artifacts in various semantic groupsfor pursuing, profiling and segregating of cyber affected entities. Thesegregation of such entities may include gating, network disconnect, DNSmarking (e.g. based on DNS tools, APIs etc.), blacklisting, recordexpiration, deletion, update of network routing and so forth.

It is to be understood that the techniques explained before may be usedto alleviate attacks and/or attacks on compromised sensing components(e.g. laser attacks on cameras, photodetectors; RF jamming etc.).Further, the system may use cyber condition and associated factors (e.g.risks) to adjust, pursue and/or not-pursue actions (e.g. index and/orapply torque vectoring in a particular way if the cyber risk conditionis high, index speed if cyber risk is low etc.).

In further examples, where semantic inference on users (e.g. onoperators, pilot, drivers) and/or attackers is available the system mayuse semantic analysis on those entities to further determine factors,indexing and further actions. In some examples, the system performssemantic analysis on an operator state based on information receivedfrom on-premise, on-board and/or wearable devices, cameras and/or othersemantic fluxes.

An architectural and deployment approach is to have the semantic cybermodel running in a separate semantic cyber unit which interacts with theoperational semantic unit through semantic exchanges. As such, thesemantic cyber unit may interrogate the operational semantic unit fromtime to time in order to assess the validity of behavior, the correctapplication of principles and laws, hence assessing the sanity of thesystem.

The semantic cyber unit performs cyber inferences and communicates withother units via semantic fluxes.

The semantic cyber module may act as a validator of the semanticinferences by the operational semantic entity. In an example the cyberunit or units initiate semantic goal-based inferences via semanticgating with the operational unit or units. Further, it uses suchgoal-based inferences for validating the sanity of the units. In someother examples the system creates semantic groups of operational unitsdesignated as cyber units to test the sanity of operational units orgroups of operational units.

The semantic cyber module may provide and/or enforce access controlrules on various components, devices' resources, data units, parts ofmemory, networking, firewalls and such. Thus, the semantic inferencesmay be used as access control rules for resources, data, processing,rules, communication and other artifacts.

In one example a semantic cyber module running on a mobile device whichreceives semantics associated with elevated alerts for a range of IPaddresses may update its semantic cyber model with acquired and/ordetermined semantic artifacts (e.g. high-risk semantic groups).

If the semantic cyber modules are connected or are part of firewalls,DNS, routers, and other network and/or computer components then thesystem may update the rules or tables of such components or control I/Odirectly (e.g. via digital blocks/components/interfaces, analogblocks/components/interfaces, packet filtering, protocol filteringetc.). In some examples, the system eliminates, marks, invalidates,netmasks and/or create block rules for malicious IP addresses and/orsemantic groups thereof; in addition, only artifacts associated withparticular semantics are allowed to pass (e.g. text (TXT) files, htmlfiles etc.). Analogously, the system may create allow or validationrules for trusted IPs and/or groups thereof. The system may use suchtechniques to update the domain name service (DNS), routing and/orfirewall tables of operating systems (e.g. Linux kernel tables etc.).

Additionally, the semantic cyber engine may use input from a variousrange of sources including sensor or human input. The owner of a devicemay specify via user interfaces that may trust or distrust a source ofinformation via semantics. As such, the system may assign cyber riskindicators related with that source of information (e.g. semantic flux)and use it for semantic inference to derive factors or any othersemantics. For example, the user may specify that doesn't trust acertain source. The system may assign low weights/factors and/or highrisk/factors to that component and as such the semantic composition maytake different fusion routes or paths. As such, the semantic fusion andcomposition may take into the account the source of semantics or thesource of the data on which a semantic is based on.

In another example in order to improve security in systems that have thepotential for being compromised through query injection the semanticengine may be coupled with a database query firewall for increasedsecurity. As such, as each query statement is issued to the database thedatabase query firewall reports to the semantic engine the querystatements being issued to the database; the semantic engine infers ordetermines various semantics based on the query components including thetype of query, columns, parameters, data type, source, user, accessrights, time, date and any other data. The system may also use thesemantics associated with the source and/or user. The semantic enginemay detect that the semantic view is in is incompatible with the type ofsemantic discovered just because a semantic route is non-existent, or asemantic route or composition exists that signify that the querystatement may be a potential risk or breach. As such, the semanticsystem infers a semantic of rejection and/or commands the query firewallto reject and/or block the request.

Examples of query injection include SQL injection and any other querylanguage that can be delivered through injection techniques via userinterface or other interfaces techniques.

The semantic cyber entity may function on a separate hardware module orcomponent. The hardware module and component may have a computing unit,memory and other components needed to support the cyber inferences. Insome examples, the memory stores the cyber unit semantic model and thecyber unit firmware. The cyber unit may update its firmware or semanticmodel from time to time in order to keep up with the applicable semanticrules, principles and laws. Cyber units may be connected via semanticfluxes.

For example, if the cyber unit is connected or coupled to a roboticsemantic unit then the cyber unit may contain semantic rules and valuesof the sensors that infer hazardous consequences. Also, hazardoussemantics may be inferred based on the core principles and rules encodedor modeled in the cyber unit. The laws of the land can be coded andmodeled into cyber units and be updated when the location of the cyberunit changes and hence the laws of the land change.

The cyber hardware module may be specialized to execute the verificationand validation of semantics with the semantic cyber model includingsemantic rules. Alternatively, or in addition, it may comprise generalprocessing units like general purpose processors, memory, fieldprogrammable gates arrays, application specific integrated circuits,system on a chip or any other components.

The cyber hardware may have wireless communication capabilities in orderto communicate with the infrastructure.

The system may ingest threat data from external sources and feed thedata to the semantic model.

Once a vulnerability, signature and/or pattern is inferred/ingested thesystem updates the semantic model in memory.

The model may comprise behavioral patterns of execution, threads andother contextual data. In an example the system comprises artifacts thatmap patterns of operation execution (e.g. via semantic model, semanticroutes, semantic time, semantic rules etc.). Thus, the system may useoperating system APIs and inspection tools coupled with semanticanalysis to analyze authorizations, logins, code and operations andprovide semantic access control.

Further, the model may comprise network traffic and protocol rules thatcan be used, for example, with deep packet inspection, network andprotocol sniffers.

Further, routers, firewalls and other networking gear may beinstrumented with semantic agents and/or units.

As such, by instrumenting the monitored network with semantic tools thesystem achieves high levels of automation and improved resilience.

The model may be coupled with location-based information that allowidentifying the trusted connections based on the semantics of movementlocation and communication patterns.

When a semantic artifact is deemed as not valid the cyber unit may takeparticular actions including isolating the devices, sensors, stream ofdata, semantic fluxes or components that were used in inference; it mayalso communicate with other systems in order to inform of the potentialof a breach or anomaly. In a particular example, the communication maytake place via semantic fluxes. The cyber unit may implement the cyberdefensive protocol described before as target (group) isolation,segregation, profiling, vetting, packing etc.

The system may use semantic trails and routes inferred before and afterthe cyber infection semantics to perform semantic analysis and learning,potentially to the point in time when the cyber infection occurred or tocurrent time. The cyber units may be linked via (semantic) cloud,fluxes, streams, point to point or mesh connectivity. Also, the cyberhardware may have semantic wireless communication capabilities in orderto communicate with the infrastructure.

The system uses access control rules to control thevalidation/invalidation of semantics via block, allow or control rules.Further the system uses semantic drift and orientation to determinehazard and/or risk semantics, factors and indicators.

The cyber unit may be modeled based on a validation approach, whereinthe cyber model is used with validate artifacts (e.g. indicators,factors, routes, orientations etc.) on the semantic inference on themonitored semantic units; in the invalidation approach, the cyber unitmodels invalidation artifacts. Alternatively, the cyber unit may bemodeled or comprise for both validation and invalidation.

The cyber and/or semantic unit may be coupled with a semanticauthentication system based on biometric data, certificates, keys, TPMs(trusted platform modules), sensorial, password, location and/orblockchain.

It is to be understood that the term “system” used in this disclosuremay take various embodiments based on the contexts as disclosed. In someexamples, “system” may represent, but not limited to, a post, a semanticcloud, a composable system, a semantic engine, a semantic networkedsystem, a semantic memory, a semantic unit, chip, modulator, controller,mesh, sensor, I/O device, display, actuator, electronic block,component, semantic computer and any combination thereof.

Further, any functionality implemented in hardware may be implemented insoftware and vice-versa. Also, functionalities implemented in hardwaremay be implemented by a variety of hardware components, devices,computers, networks, clouds and configurations.

We exemplified how the system may optimize budgeting. In furtherexamples of budget optimization, the system may challenge resonantsemantic groups with ways of applying discounts and/or available offersrelated to a purchase challenge (e.g. buy a track ticket for 10$ untilbreakfast tomorrow). While the system may challenge track and/or ticketproviders it may also challenge for discounts, coupons (providers) as(“discount”) (“coupon”) may be comprised in a semantic route/rule,diffuse and/or resonate with the user's goal, route, (goal's) leadershipand/or related inferences. In an example, the system may infer goalleaderships comprised in the goal semantic route (e.g. “buy”, “track”,“ticket” and/or compositions of those) which may be used to furtherinfer providers of coupons and/or discounts for particular goalsleadership and further, challenge the providers with the correspondingdiscounts (e.g. give me a price for track ticket by applying thediscount code HAPPY_MEAL, give me a price for track ticket by applyingthe discount code from <provider_discount_coupon>, give me a price fortrack ticket by connecting to <discount_provider_name> etc.).

The discounts are inferred base on semantic groups and made availablethrough semantic access control and/or gating.

In further examples, the ticket provider advertises the discounts itselfand/or automatically applies discounts based semantic analysis, semanticidentities (e.g. of purchaser) and/or semantic groups thereof; it is tobe understood that the coupons may be based and/or applied based onsemantic time, semantic indexing, hysteresis and/or damping.

The system may use bargaining when purchasing. The bargaining may bebased on undershoot and/or overshoot type of inferences (e.g. the budgetand/or offered price is between overshoot and/or undershoot). Theundershoot and/or overshoot bargaining may be also based on suppliers(collaborators) and/or market circumstances. In some examples, thecircumstances and/or behaviors may be inferred as intrinsic, offensive,defensive and/or neutral. When the circumstances are intrinsic withoutmuch projected drift the system may follow the semantic trails moreclosely.

The system uses offensive/defensive, friend/foe and/or further semantictime analysis to determine and/or bargain for the best deals and/orissue purchase orders.

The system may use a motivation and/or further satisfaction factors inbargaining type inferences. In some examples,

The system may not specify a budget, case in which the system looks forthe optimal price within further restrictions, locations, constraintsand/or semantic time (e.g. get a reasonable priced track ticket in thelower section for tomorrow's game, get the best not overpriced or maybeslightly overpriced two tickets for tomorrow's game, get me a ticketthat will entertain me tomorrow (in Charlotte) etc.).

It is to be observed that the system may look for price tickets betweenan overshoot and/or undershoot range (e.g. for “best available” uses asmoothed overshoot orientation based on offensive behaviors; “bestreasonable or not overpriced” may use a range between an undershootorientation based on offensive behaviors and/or an overshoot based onneutral and/or defensive behaviors). Constructs of the request (e.g.“maybe slightly overpriced” having a deviation from the intrinsic or theprevious orientation—not overpriced/reasonable) may be used to factorizethe user's desire/likeability for attending and/or being in a locationat a particular time; further, the system may infer and/or use dampingand/or hysteresis for achieving desirability and pricing goals.

The system may intrinsically determine the localization and/or mappingof the goals at endpoints (e.g. tomorrow the system knows by a schedule,place ticket or other inferences that the user will be in Charlotte soit may need to look for tickets in Charlotte).

Analogously with bargain type interfaces, based on overshoot and/orundershoot, the system may localize, map, anchor and/or determineoptimized locations and/or endpoints within the semantic model; it is tobe understood that such optimized locations and/or endpoints may bemapped within the hierarchal structure of the model at various levels.In further examples, the system determines a mapping, anchor and/orlocation based on undershoot/overshoot intervals and/or furtherintersections in elevation and azimuth.

The system may gain budgets by issuing orders and/or acquiring financialinstruments, currency, stocks and/or other trading items on financialmarkets, trading markets, electronic currencies networks. In someexamples, the trading items are (semantic) time and/or further budgets.In further examples, the system allocates budgets for particularsemantic time (intervals).

The system may wait to acquire budgets and/or to perform the inferenceand/or the actions within the required and/or resonant budgets. In someexamples, the system may bargain and/or wait for some costs to go downand/or for promotions to occur.

In further examples, the system is challenged and/or challenges to “buythings that I like” and as such may prefer things which are moreresonant factorized for “like”/“preferred” (and/or related synonymsand/or groups).

For challenges such as “surprise me” the system may prefer things closerto decoherence and/or borderline resonant for artifacts which projectaffirmative resonance and/or further “surprise” (and/or related synonymsand/or groups).

The system may choose a lesser number of routes, attributes, indicatorsand/or factors to be resonant and/or less shifted for “like”/“preferred”while may chose a larger number to be less resonant, more shifted and/orwith more spread for “surprise”. It is to be understood that in generalthe system may chose higher (e.g. primary, secondary etc.) leadershipartifacts for “like”/“preferred” and lower (e.g. secondary, tertiaryetc.) leadership artifacts for “surprise”; such leadership promotion maybe based on semantic indexing and/or biasing.

Further, the system uses projections and thus, even if it may not useleadership and/or resonant semantic artifacts at first, the inferencemay progress towards inferring leadership semantic artifacts associatedwith the particular profiles and/or semantic identities which allow“like”/“preferred”/“surprise” resonant inferences.

The system may have limited budgets and allocate those budgets based onleadership inferences and/or goals. In some examples, the systemallocates budgets to leadership inferences determined by projectedconsequences factorizations.

The system infers restrictions and/or constraints during semanticinference. In some examples, the constraints and/or restrictions may bebased on its own capabilities and/or semantic profiles, semantic time,factorization thresholds, goals/sub-goals and/or further artifacts. Theconstraints/restrictions may be hard (e.g. very (99%) unlikely (99% notlikely) to succeed, not possible and/or very riskycircumstances/behaviors if the constraints/restriction are not followedand/or considered) and/or soft (e.g. more relaxed factors).

In some examples, the system associates hard constrains/restrictionswith hard semantic rules and soft constraints/restrictions with softsemantic rules.

The system may use indexing, hysteresis and/or damping to adjust theinference associated with the constraints and/or restrictions (e.g. forinferred soft constraints using a more offensive/leisure/diffusivebehavior while for hard constraints using a moredefensive/cautious/non-diffusive behavior; further, for soft constraintsinferring/applying larger risk indexing/thresholds/hysteresis and forhard constraints inferring/applying lower riskindexing/thresholds/hysteresis etc.).

The semantic smoothing may be based on projected inferences in rapportwith defensive and/or offensive behaviors. In some examples the systemmay bias the offensive and/or defensive behaviors based on theassessment of the projected budgets and/or further factors (e.g. risk,reward etc.).

The offensive and/or defensive behaviors of leaders which woulddetermine high confusion within the leader's group in rapport with thegroup's purpose and/or its associated semantic artifacts may determine achange of leadership. It is to be understood that the high confusion maybe determined based on a group's confusion threshold interval. Further,refactorizations of fluxes in the group may determine some of themembers to leave the group once the factorization of the group flux doesnot comply with the confusion interval.

In some examples of traffic control the system may biases in varioussections particular behaviors associated with particular semanticgroups. In an example, at an endpoint (e.g. associated with a trafficstop, intersection and/or hierarchy thereof) the system may detect thatthe offensive and defensive behaviors are unbalanced and thus it mayadjust the flows and/or signaling based on the behaviors and/or tobalance/neutralize the behaviors. For example, for offensive behaviorsit may infer and/or adjust (index) for a shorter green traffic lightand/or a longer switching to green for the crossing traffic while fordefensive behaviors may apply a high drift/entropy inference (e.g.longer green light, shorter yellow light and/or shorter switching).

The system may increase the semantic spread and/or adjust focus byallowing more relaxed access control, diffusive and/or further semanticrules; in some examples, the system disables altogether particular softaccess control rules. The system may adjust the diffusiveness by varyingthe same factors/indicators and/or associated rules in variousconfigurations. In some examples, such generative behaviors may be usedwhen budgets are high and/or when generating new goals, transferknowledge and/or borderline resonances.

The system may increase the diffusion and/or relaxation of rules whereinthe system factorizes (e.g. increases) satisfaction, trust, leisure,affirmative factors in rapport with semantics and/or (associated) rules;alternatively, or in addition it may decay (e.g. decrease), index and/orbias the thresholds for such satisfaction, trust, leisure, affirmativefactors. Analogously, the system may decrease dissatisfaction, concernand/or stress factors in rapport with semantics and/or (associated)rules; alternatively, or in addition it may increase, index and/or biasthe thresholds for such dissatisfaction, concern, leisure and/or stressfactors.

The system may use high (entangled) entropy (a.k.a. H/ENT) actionsand/or thresholds (e.g. INCREASE/DECREASE, ON/OFF etc.) in rapport withhigh (entangled) entropy indicators (e.g. SATISFACTION/DISSATISFACTION)and thus when a first indicator and/or associated threshold is increasedand/or enabled (e.g. ON) in rapport with a semantic identity and/orartifact the high (entanglement) entropy indicators and/or associatedthresholds may be decreased and/or disabled (e.g. OFF) and/orvice-versa. In similar fashion the semantic ALLOW/DO rules and/or routesmay be factorized and/or enabled (e.g. ON) while the high entangledentropy rules BLOCK/DO NOT rules and/or routes may be reverse factorizedand/or disabled (e.g. OFF) and/or vice-versa. It is to be observed thatthe high (entanglement) entropy reflects in the enablement semantics(e.g. ON/OFF).

When the budgets decay (e.g. below a threshold), spread is high and/orthe confusion is high (e.g. over a threshold) the system may adjust to amore restricted access control, diffusive and/or further semantic rules.Further, it may invalidate semantic artifacts associated to increasedspread and/or confusion. Such critical behavior may decrease thesemantic spread.

The system challenges and/or caches identification artifacts from thesematic cloud based on locations. As such, the identification artifactsare cached at endpoints based on projected inferences which comprisesuch endpoints (e.g. based on shifts, drifts, diffusion etc.).

In some examples, when the system changes the semantic field environmentand/or roams from one location to another (e.g. changes rooms,buildings, legislations etc.) it may decay associated artifactsassociated with the previous semantic field environment within asemantic view; further, the confusion may be elevated at first until thesystem establishes coherency and/or reduces confusion in the newenvironment.

The system may adopt a more generative behavior when entering a newsemantic field context and/or location; further, it may follow a morecritical behavior after a semantic time in the new semantic fieldcontext/view. It is to be understood that in a generative behavior thesystem generates inferences projecting less consequences; in a criticalbehavior, the system invalidates generated inferences by projecting moreconsequences.

In some examples, the system uses advertising and/or publishing goals(e.g. based on user input, semantic profile etc.).

The popularity and/or leadership of a particular artifact may increaseas it induces (affirmative) coherency and/or resonance within (related)semantic groups. Further, the system may diffuse and/or affirmativelyindex other factorizations of their capabilities (e.g. the system maydiffuse and/or index other capabilities than the original leadership)based on (particular) observer semantic profiles and/or resonantsemantic profiles.

As leader's popularity increases, the costs and/or budgets associatedwith accessing those leaders and their associated semantic artifacts mayincrease.

The system may use affirmative resonance, semantic time managementand/or semantic indexing to adjusts factors, costs and/or budgets.

The system may bias and/or index loss goals by using hysteresis and/ordamping. Decayed affirmative budgets and/or factorized loss (e.g.increased loss factors) of affirmative budgets may be associated withincreased dissatisfaction, concern and/or stress factors. Analogously,potentially by (entangled) entropy inference (e.g. ofincreased/decreased orientation, affirmative/non-affirmative, gain/lossetc.), decayed non-affirmative budgets, and/or decayed loss factors ofaffirmative budgets may be associated with increased satisfaction and/orleisure factors. By further (entangled) entropy inference, factorizedaffirmative budgets and/or factorized gain (e.g. increased gain factors)of affirmative budgets may be associated with increased satisfactionand/or leisure factors. Even further, factorized non-affirmativebudgets, and/or decreased gain factors of affirmative budgets may beassociated with dissatisfaction, concern and/or stress factors.

It is to be understood that the affirmative budgets refers to thebudgets and/or (projected) investments which have affirmative resonanceand/or positive polarity in rapport with a semantic identity;analogously, the non-affirmative budgets refers to the budgets and/or(projected) investments which have non-affirmative resonance and/ornegative polarity in rapport with a semantic identity.

In case that a sub-system receives a request for inference with aspecific budget, the sub-system executes an evaluation of the goal (e.g.based on what-if and/or projected semantic routing and analysis) formeeting the inference (e.g. GIVE ME ALL YELLOW CARS SPEEDING UNTIL JOHNSHOWS UP or SHOW ME UNTIL JOHN GOES HOME THE TEN BEST PLACES TO CONCEALA YELLOW CAR WITHIN TEN MILES OR TEN MINUTES FROM A/THE COFFEE SHOP). Assuch, the system may be provided with a goal budget (e.g. best places toconceal) and so the system may project based on the specified and/orinferred budgets; further the goal leadership being CONCEAL with asemantic identity of YELLOW CAR the system may look for artifacts whichobscure and/or mask the semantic identity of YELLOW CAR. Further, thesystem may associate a budget of 10 minutes to the CONCEAL inferenceand/or goal and a further drift from coffee shop endpoints. While afurther leadership semantic may comprise DRIVING because of the CARsemantic identity the system may consider other options if the DRIVINGrelated projections are not within the budget and/or the risk factorsare high; in some examples the systems may consider forming a semanticgroup (e.g. for TRANSPORTING, PLATFORM; LIFTING etc.) with anotherobject of another modality of transportation (e.g. RAILWAY, CAR; AIR,HELICOPTER etc.) and use projected inferences on such routes and/orgroups.

While in some presented examples the system determines unusualobturations and/or behaviors (e.g. broken lens, dirt present, blindingattack etc.), in other examples it may infer a normal obturation (e.g.the lens is covered for protection to secure it against dirt, breakingin, blinding/mesh damage and/or further to reduce processing, put thesensor to sleep etc.) and as thus it may pursue semantic memory and/ormesh optimization based on semantic analysis. It is to be understoodthat the lens protection and/or normal obturation inference may be basedon a lens cover transducer/actuator sensing/control and/or furtherinference and/or control based on access control rules, semantic timemanagement and/or further semantic analysis.

The system may predict weather based on the sensor data (e.g. Dopplerradar, polarization radar etc.). As such, the system projects thesemantic indexing and/or diffusion of the radar inputs and/or associatedgraphs/graphics/colors to the radar maps and use them in the carriersystem guidance and/or further semantic augmentation.

In a previous example, we explained that when the time management ruleis exclusive (e.g. 100% EVERY MEAL WITH MEAT) the system may not pursuethe current MEAL drive inference, perform challenges and/or furtherinferences on alternate trails, routes and/or semantic groups. Infurther examples, the system may challenge food provider fluxes fornegotiating and/or budgeting the projections, goals, inferences and/orsemantic time management entries.

As it is observed, the semantic artifact EVERY MEAL WITH MEAT comprisesthe discriminator EVERY which may be used as a discrimination bias incurrent and/or further inferences based on the factorization inferredafter such experiences.

Discrimination factors and/or biases may be inferred in the semanticfield to accurately infer and/or track semantic identities. In someexamples, the system infers discriminatory factors (of) (and/or) groupsof semantic indicators, semantic identities, DNA signatures; furtherfeatures, parameters, zones, movements may be associated and/or be usedfor discrimination factor inference.

The system may use semantic leadership inference for inferring and/orachieving discrimination indicators and/or factors. In some examples,the system comprises semantic rules and routes which diffuse, blockand/or do not allow discrimination factors related to semantics of race,gender, age, sexual orientation etc. In some examples, thediscrimination based on such factors are blocked at higher hierarchylevels, further semantic augmentation and/or challenges.

In some examples, the system infers discrimination (leadership)semantics which are used as discrimination indicators.

The system may use semantic leaders as discriminators. Further, when thediscrimination inference (e.g. comprising semantic artifacts, resonantsemantic groups etc.) have high entropy, drift, shift and/or biasagainst fairness inference (e.g. based on ETHICS rules and/or routes)then the system may determine decaying of leadership factors.

Discrimination factors may be associated with indicators such as EVERY,ALL, SOME, MAJORITY, NONE, FEW. The discriminator factors may becorrelated (e.g. EVERY MEAL WITH MEAT semantic route may comprise 80%MAJORITY MEALS WITH MEAT, FEW MEALS WITHOUT MEAT; 20% ALL MEALS WITHMEAT, 80% NO MEALS WITH MEAT. etc. Such correlations may also be basedon high (entanglement) entropy.

The system may comprise semantic rules to factorize, adjust, DO/ALLOW,DO NOT/BLOCK and/or gate discrimination factors, biases and/orassociated artifacts (e.g. images, documents, zones, UI controls and/orfurther multimedia and/or semantic artifacts).

The intrinsic capabilities, purpose and/or behavior and further the(entanglement) entropy of (composite) semantic inferences in rapportwith the former may be used to denoise and/or factorize inferencesincluding further actions. In an example, a device associated with an“alarm” semantic identity has intrinsic capabilities to “keep operatingroom safe” and thus when the device detects an unusual behavior and/orevent (e.g. with high drift and/or entropy from the intrinsic safecapabilities) it may infer that room is not safe anymore and furtherthat the alarm intrinsic behavior is switched “off” and thus inferring“the alarm went off”; it is to be understood that <the alarm> in theprevious example refers to a semantic identity. Further, it may inferhigh (entangled) entropy remedies, actions and/or semantic identities(e.g. providing required capabilities—e.g. operating room sprinkler) inorder to return to the intrinsic safe behavior by inferring and/orapplying various routes and/or rules (e.g. “spray halocarbons”,“activate (the) (operating room) sprinkler” etc.).

The system infers risks and/or threats factors based on goals, missionsand/or profiles. In one example, the goal associated with a camera is tokeep an area safe from a security based identity profile perspectiveand/or semantic view, while of an intruder to keep the area safe from anintruder identity profile perspective and/or semantic view. It is to beobserved that, while some of the goals may be the same for both profiles(e.g. STAY SAFE, MAKE MONEY) from an entangled and/or causal route/groupand/or semantic view they are opposite, have high (entanglement) entropyand/or are non-affirmative resonant because the semantic profiles andsemantic artifacts thereof which guide the actions and/or operations onhow to achieve the goals and/or missions have high entanglement entropy(e.g. STEAL GOODS, EARN MONEY BY SELLING GOODS vs EARN MONEY BY WORKINGetc.). As such, particular sematic profiles (e.g. of OWNER) are assignedleadership while denying access and/or leadership to profiles which havehigh entanglement entropy and/or are non-affirmative resonant. It is tobe observed that while for the intruder or victim one of the goals is toSTAY SAFE its projections and/or further actions cause (high shift,drift and/or entropy) UNSAFE inferences in other semantic identitiesand/or semantic groups and thus non-affirmative resonance is realized.Further, the system may factorize foe indicators based on perceivedoffensive behaviors and/or hostility. The system may compose affirmativeand/or non-affirmative resonances; in some examples, if the motiveand/or circumstance of the intrusion is affirmative resonant with thevictim semantic identities and/or further semantic profiles (e.g. NEEDTO BUY FOOD) then it may decay the non-resonance in regards to thesemantic identity of the intruder; however, if the victim is projecting(e.g. based on its profile and/or intruder's profile) that the intrudercould have been achieving the same goals by using other orientationsand/or semantic artifacts which were feasible using intruder's semanticprofiles then the non-affirmative resonance may be further factorized.

It is to be observed that an entity may have multiple semanticidentities and thus multiple semantic profiles. During semanticanalysis, the system uses the leadership semantic identities and/orprofiles based on circumstances and/or uses further techniques to reduceconfusion and/or superposition; these may occur due to inference on thesemantic artifacts associated with the semantic identities, semanticprofiles and/or further semantic (leadership) hierarchy.

In further examples, the intrinsic behavior and/or guidelines arespecified by the user.

The system may infer that certain semantics and/or constructs decaysindicators associated with a composite construct comprising thesemantic. In an example, the term BUT may determine indicators whichhave a different influence on the entropy within the route comprisingthe term. The term BUT might be used as a conjunction, preposition,adverb or noun. In most constructs it may cause the factorization of adiscriminator and/or leadership related to further composite (projected)inferences. In some examples, the system generates a comparison of afirst part of a route with the second part of the route and determinesthat the part following closer to the term is emphasized and/orfactorized as a leader and/or discriminator in further inferences. Insome examples, the parts of the routes are deemed highly entropic andthe system uses the term to emphasize the sub-route, artifacts and/orsemantic identity associated with BUT (e.g. I CAN EAT MEAT BUT BETTERNOT—NOT eating meat is leader BECAUSE I AM FASTING, IS ALL BUT HIM—HIMis leader over others etc.). Highly entropic constructs may increase thesuperposition in self and/or collaborative parties; if the superpositionis coherent collapsible and/or resonant it may have factorizing effectwhile if it is not coherent collapsible it may have decaying effectand/or factorize/increase confusion.

It is to be understood that in some augmentation examples, some parts ofthe routes are implicit and may not be rendered, displayed or writtenbut instead may be expressed as part of an inferred composite semantic.

As mentioned, the system may deny particular operations and/or semanticsin a route.

In some examples, semantic resonance is based on coherent inferencesbetween semantic routes.

The posts and/or other vehicles may use the friend and/or foe (a.k.a.friend/foe) identification to project the best routes to follow.

The friend/foe may be associated with semantic identities and/or furthersemantic artifacts.

The system integrates and/or renders various views and/or UI controlscomprising streams, fluxes, windows, players and/or any other renderersand/or streams of videos, multimedia, frames, electromagnetic and/orother sensing data; further, the system analyzes the inputs and augmentthe viewer (e.g. user, group, sensor, robotic device etc.) based on itsown semantic profiles. In further examples, only the leader and/orcreator of the views and/or presentation can visualize the smartnarrative; in further examples, the leader has access to otherstreams/fluxes/windows/players/renderers semantic profiles and it can besemantically augmented based on those semantic profiles and furtheradjusts the guidelines, routes, narrative and/or behavior based on that.Further, a user may select the views and/or associated semanticidentities and allow the distribution of semantic augmentation to thoseviews and/or fluxes; in addition, the semantic augmentation can begated. Further the system may specify and/or select the artifacts and/orassociated semantic profiles which should compose and perform smartnarratives based on such compositions. The semantic profiles may beassociated with the views and/or with semantic identities associatedand/or inferred from the view/flux/stream data.

In further examples, the system may identify friend/foe in theenvironment, presentation and/or rendering comprising multiple views andas such it allows the semantic augmentation to be performed based onsuch inferences (e.g. allow its semantic augmentation to be shared withfriends; allow a high entangled entropic augmentation to be shared basedon friend/foe; diffuse its semantic augmentation with friends and/orfoes etc.).

The system may refresh displays and/or semantic views based on semantictime and/or further friend/foe. Further, it may control sensors,actuation, gating and/or further semantic augmentation based on suchinferences. In some examples, the system sends notifications and/orchallenges users/owners when inferring friend/foe.

The system may use projected inferences to avoid and/or to follow hardlydiffusible routes as determined based on foes; such routes are hardlyreachable at particular semantic times as projected by the system.Analogously, by high (entanglement) entropy, the system may preferand/or follow easily diffusible routes in rapport with friends. In someexamples, foes are associated with restrictions in rapport withparticular trajectories.

The system may infer friend/foe based on offensive/defensive behaviorsand/or block/allow inferences. In an example, a carrier may determinethat another vehicle has narrowed a dock door on purpose in order toblock itself (the carrier) and/or associated resonant semantic groupsfrom passing and/or further achievement of their goals. As such, theother vehicle is being deemed and/or factorized as foe by the carrierand further being non-affirmative towards the carrier's goals and beingperceived as hostile (e.g. because uses offensive behaviors to blockinferences towards the carrier's (resonant) goals). However, if thesystem infers that the blocking is defensive (e.g. to protect itselfand/or resonant groups) then the hostility and the foe factor may bedecayed. Further, if the other vehicle actions are toward protectingand/or optimizing the carrier safety and/or its goals then the systemmay factorize the friend factors in rapport with the other vehicle.

Friend/foe inferences may further allow the system to implement fight orflight responses; the fight or flight responses may be based only onallowable actions and/or further related (entangled) restrictions. Insome examples, the system comprises rules related to “do not destroyproperty”, “do not remove foe unless permissioned by the owner” and thusit is not allowable to infer and/or act unless it has and/or receivespermission from the owner; further, the system infers that the flightand/or possible alternate (projected) routes should be used—e.g. of(become) more friendlier etc.). By high entanglement entropy, the systeminfers and/or factorizes friends when such friends allow the unblockingand/or diffusion of artifacts towards the (resonant) goals.

A restriction comprising two (entangled) artifacts determine and/orcomprise a constraint (e.g. garage door is too small for a boat—garagedoor and boat are constraint entangled); based on constraints, thesystem identifies consequences and/or further factors (e.g. risk etc.)in rapport with the endpoints, artifacts, semantic identities, users,owners and/or providers of such restrictions.

The system may project as friendlierartifacts/circumstances/environments those deemed more safe (e.g. lessthreats, lower fear, less competition etc.) and/or further beingassociated with lower restrictions/constraints.

It is to be understood that the term “less”, “lower”, “higher” and/orother comparative orientation factors are used in order to projectsituations when the system has choices and further, based on semanticanalysis, pursues some of those choices in particular ways (e.g. basedon offensive/defensive, variable stimulation, motivation,polarity/polarization etc.); the system may also pursue “reasonable”analysis when the budgets are tight.

For increased safety, the system may prefer trajectories, routes and/orfurther artifacts projecting friendlier environments with less unknownsand/or less entropy. Further, when in offensive mode and/or motivationis higher factorized the system may be biased to increase the tolerance(e.g. index target interval, damping, hysteresis etc.) for friendliness,unknowns and/or entropy.

In some examples, restrictions and/or constraints imposed bycollaborators may determine affirmative/non-affirmative,hostile/non-hostile and/or further friend/foe inferences.

The system may distrust some semantic artifacts (e.g. links, endpointsand/or semantic groups) and/or their associated semantics based onfailed expectations that those deliver within the semantic group. In anexample, the system infers and/or projects a strong affirmative(resonant) semantic group but later infers hostility within the groupand thus it increases the risk and/or decays strong affirmativefactorizations of the semantic artifacts which generated the strongaffirmative semantic group inference in the first place. Further, if thefailed strong affirmative inferences were based on hard semanticartifacts, constraints and/or relationships, the system may infer a biasto never infer strong affirmative resonances.

The system may infer counter-biases and/or challenge users and/or othercollaborators about such counter-biases.

In some examples, the system uses friend/foes inferences to discriminatebetween at least two routes, behaviors and/or situations. Further, thesystem discriminates between at least two threats, emergency and/orhazardous behaviors and/or circumstances.

In further examples, the system infers and/or pursue challenges whichare related with identifying and/or inferring causes and/or otheropportunities which project friend, foe and/or resonant inferences withother semantic identities and/or semantic groups thereof (e.g. WHAT CANI DO TO BE MORE RESONANT WITH JOHN AND JANE; WHY ARE THE DOES HOSTILE,HOW CAN I BE MORE LIKEABLE TO DOES, SHOULD I BEFRIEND THE UNDOES etc.).It is to be observed that the system may perform challenges in regardswith high (entanglement) entropic artifacts (e.g. DOES vs UNDOES, FRIENDOF UNDOES may cause LESS LIKEABLE OR FOE TO DOES which is highlyentropic to LIKEABLE TO DOES etc.).

The system may infer hostility factors based on inferences related tofriend/foe wherein the hostility factor is related to a friend and/orfoe factor; the hostility factor is proportional and/or semanticfactorized with the foe. When the hostility is inferred and/or publiclyshared in collaborator environments, groups and/or flux network then thesystem may further factorize the hostility factors and/or decay thefriend factors; alternatively, or in addition, the same factorizationpattern may occur when the foe pursues offensive behaviors on competingartifacts and/or markets.

In further examples, the system performs semantic augmentation based oninferences and further semantic analysis of a debate between varioussemantic (robotic) entities. It is to be understood that the semantic(robotic) entities may be based on various semantic profiles (e.g. ofvarious users, companies, groups, posts etc.) and they perform semanticaugmentation to the semantic identities and/or groups associated withthe corresponding semantic profiles and/or groups. The debate's semanticorientation may be based on non-affirmative and/or non-resonant semanticartifacts between the robotic entities.

The system may infer, challenge and/or present relevant facts, truthsand/or evidence supportive of an argument relevant to a challenge.Further, if the system infers that the debate is argumentative (e.g.based on foe identification, offensive and/or hostility factors) then itmay further identify friends and/or foes amongst debaters, hosts and/oraudience and pursue offensive and/or defensive behaviors. Further, thesystem may want to be persuasive and thus identifies the entities (e.g.in audience) influencing leadership discriminatory factors toward itsgoals; in further examples, the system identifies the audience as afriend and/or looks to build affirmative resonance with the audienceand/or semantic groups thereof. The system may identify theargumentative nature and/or factor of the debate based on inference of(high entropic) non-affirmative resonant semantic entities, offensivebehavior, hostility and/or foes; it is to be understood that suchindicators, factors and/or behavior may be inferred as related to itselfand/or between other entities. In some examples, the system identifiesthat JOHN is a foe towards itself (system) because JOHN debates datingJANE which is highly non-affirmative (resonant) with the system (e.g.because the system likes JANE and have a leadership goal to date/connectwith JANE). Further, the system identifies that JOHN is hostile towardsBILL because JOHN uses preponderant offensive behaviors to argumentagainst BILL's and thus, it may look to build resonance with BILL todebate JOHN.

In some examples, the system quantifies and further factorizes apersuasiveness factor based on projected resonances and/or (their)further diffusion factors of its goals. The system strategic leadershipgoal may comprise the factorization of persuasiveness by factorizingfriend/foe towards FRIEND (e.g. FRIEND 51% vs FOE 49%) in targetedsemantic identities and/or semantic groups at particular semantic times.

The system may be biased to respond to challenges by preserving a higherconfusion and/or drift from challenger's expectations/goals when theinitial challenge was in forms which projects less choices and furtherprojects uncertainty, non-friendly and/or non-resonant inferences. In anexample, the system is challenged with DO YOU HAVE 2 QUARKS? and thus,because the challenge and/or circumstances are hardly believable,un-friendly and/or non-resonant the system may challenge respond withDON'T KNOW WHAT QUARKS ARE in order to reduce the semantic time and/orfurther unknown/risks/threats. However, if the system infer that thechallenge and/or circumstances are friendlier then it may challengerespond with CAN'T GET A HOLD OF QUARKS in order to preservefriendliness and/or resonance.

The system may project that some challenges have negativepolarity/influencing and/or are distractive (e.g. based on a distractionfactor which is inferred as the following) from pursuing a previouslyestablished (resonant) goal in a semantic time and/or semantic budget.The challenge and their associated projected inferences are increasingthe semantic spread, related superposition and/or confusion whiledecreasing resonance (and/or increasing non-resonance) in the currentleadership semantic view and, further, threatening the budgets andfurther realization of the goals. The system may already pursue highlyfactorized routes toward (pre-committed) goals with little projectedconfusion and thus challenges which project distraction and/or furthersemantic drift and/or shift may be gated, blocked, routed, redirectedand/or postponed. (e.g. “remind me later after I finish the analysis onS2P2 health about Bill's challenge on quarks”, “please ask my coach S2P2about quarks” etc.).

Distraction factors may be used to determine liabilities and risks whenhazardous circumstances occur. Further, the system uses distractionfactors to determine risks associated with guarantees.

The system may pursue goals and/or sub-goals for acquiring, beingassociated and/or maintaining a particular semantic identity.

The system may determine and/or implement more tolerant behaviors byusing neutral, intrinsic and/or defensive behaviors when inferring foesand/or hostility.

It is to be understood that the system may infer, be instructed and/orcomprise semantic rules and/or routes which would control, constrainand/or block the system from identifying foes, use offensive behaviorsand/or become hostile in particular circumstances (e.g. constrain and/orblock inferences relating with dating, connecting and/or receivingcapabilities/channels/routes/budgets from JANE and/or other semanticgroups, do not infer and/or factorize hostility etc.). Alternatively, orin addition, the system may infer, be instructed and/or comprisesemantic artifacts which would determine more strict behaviors towardsitself and/or more tolerant towards others in particular circumstancesand/or related to particular semantic entities.

The system may implement more tolerant behaviors by using neutral and/ordefensive behaviors against foes.

The system may use non-affirmative resonance to infer friend/foesemantic identities, factors/indicators, (product) goals and behaviors.While the friend/foe goals may resemble the system's own drivesemantics, orientation, goals and/or semantic routes the resonance basedon such goals is deemed non-affirmative when the semantic identities arefoe and thus determining high (entanglement) entropy and/or beingassociated with dissatisfaction, concern and/or stress factors.

The dissatisfaction, concern and/or stress factors may be factorizedbased on (fear of) loss/decaying/indexing/dissociation (e.g. of resonantgroups, leadership, goals, position, semantics, budgets, kinematics,trajectory, orientation, stability, predictability, diffusion etc.)and/or (fear of) gain/factorize/indexing/association (e.g. ofnon-resonant leadership, groups, semantics, indicators, diffusion etc.).Analogously, likeability, preference, satisfaction, trust, leisureand/or affirmative factors may be factorized based onloss/decaying/indexing/dissociation (e.g. of non-resonant leadership,groups, semantics, indicators etc.) and/orgain/factorize/indexing/association (e.g. of resonant groups,leadership, goals, position, semantics, budgets, kinematics, trajectory,orientation, stability etc.).

The fear of loss/dissociation and fear of gain/association may berepresented and/or coupled based on entanglement wherein the measurementand/or collapse of loss/gain artifacts may determine and/or collapse theentangled gain/loss artifact—e.g. (loss of) stability and/orpredictability (e.g. stability of a post as measured by at least onemultiple axis accelerometer/gyroscope/accelerometer) may be entangledand/or determine (gain of) risk and/or vice-versa, stability of economicgoals may be negatively affected by un-stability of a pandemic etc.

The system may exhibit short term confirmation bias. As such, the systemmay be biased towards applying and/or being LIKELY to apply cachedroutes whenever new inferences occur and thus bias the projectedinferences toward such artifacts. In such cases the system may apply abias to decay the factorization of such routes based on the inferenceswhich increase the semantic spread in the network.

Stability and/or predictability comprise and/or are generic indicators(e.g. indicating the stability/predictability of stock indices,macro-economic indicators, stability/predictability of localizedvoltages (based on environment, semantic time etc.),stability/predictability of diffusion etc.).

The system may exhibit semantic resonance when inferring behaviorsand/or situations in semantic views, scenes and/or further semanticidentities.

Stability factors may be used to factorize and/or index fluency factors.

The system may use friend/foe identification and/or factorization topursue groupings, negotiations, goals and/or missions. In some examples,the system may infer and/or factorize friend artifacts based on(projected) (entangled) inferences on foe artifacts goals, productsand/or associated semantic attributes. It is to be understood that insome examples the friend/foe factors may create confusion and/orsuperposition (e.g. both friend/foe indicators are closely factorized)and as such the system uses confusion, superposition and/or semanticreduction techniques.

It is to be understood that the system may use a composite (entangled)indicator for friend/foe which may further comprise an indicator foreach friend and foe. When mentioning friend or foe it is to beunderstood that it may refer to the respective component indicatorand/or to the bias of the composite indicator towards the mentionedcomponent indicator.

In some examples the foes are used to infer and/or represent competingartifacts while the friends are used to infer and/or representnon-competing artifacts (e.g. semantic identities, goals, routes, rules,endpoints, skills etc.).

The system may identify negotiating and/or trade indicators, factors,margins and/or intervals thereof based on friend/foe semantic analysis.It is to be understood that such indicators and/or factors may beassociated with competing, non-competing artifacts or both (e.g. forstrategic and/or long-term goals, missions comprising a variety of goalsetc.).

In further examples the foes represent semantic artifacts (e.g. semanticidentities, semantic routes etc.) which are not recommended (e.g. to auser, group etc.) and friends represent semantic artifacts which arerecommended.

Friend/foe recommendation may be used in semantic augmentation forlearning, viewing, investing, attendance, shopping (e.g. recommend andnot recommend items for purchase), security (e.g. logging in into asystem, entering an area, following a route, accessing an item, allowingan action etc.).

The system may use friend/foe biasing to emphasize and/or further inducedirect and/or indirect inverse/reverse polarity resonance. For instance,in indirect resonance the system may use groups of semantic artifacts,trails and/or routes of resonances which determine opposite polarities.In an example of inverse/reverse polarity, the system generatesartifacts, behaviors, signals, waves, renderings and/or augmentationwhich associates a foe artifact with non-affirmative behaviors and thusby (composition of) double high (entanglement) entropy artifacts itgenerates affirmative, resonant artifacts, reverse polarity and/orbehaviors. Further, the system learns by associating known resonanceswith reverse polarity inferences.

Polarity may be associated with charge and/or voltage polarity.

In some examples the voltage polarity is modulated by semantic waveconditioning.

The magnetic field in an inductor generates an electric current thatcharges the capacitor, and then the discharging capacitor provides anelectric current that builds the magnetic field in the inductor whichfurther determines the repetition of the cycle and the self-sustainingoscillation/resonance. The system may use semantic biases, damping,hysteresis and/or indexing to adjust components' and/or circuits biases,damping and/or hysteresis and thus adjusting the self-sustainingoscillation and/or further associated semantic resonance. It is to beunderstood that the capacitor charge polarity and/or further currentconditioning in inductors may be associated with semantic factorpolarity.

Further techniques such as sympathetic resonance may be used. In someexamples, the sympathetic resonance is used to induce and/or diffuseresonance between various semantic identities, semantic groups and/orhierarchies thereof. Further, particular sub-groups and/or hierarchiesmay be resonant to only particular harmonics at a given resonantvibration, spin, damping, polarization and/or frequency. Further, thesystem may infer resonant semantic artifacts by polarizations associatedwith such semantic artifacts which induce affirmative (e.g. positivepolarity) and/or non-affirmative (e.g. negative polarity) inferences.

In some examples, a semantic identity and/or further semantic profile isassociated with a positive and/or negative polarity in rapport to asemantic artifact. Positive polarity may be used to representaffirmative artifacts and/or factors; analogously, also according withthe high (entanglement) entropy, the negative polarity may be used torepresent non-affirmative artifacts and/or factors.

In some examples, the system uses polarity inference to determinepolarization in resonant semantic groups. Analogously, the system usespolarization of semantic groups to determine group and/or furtherresonant polarities.

The system infers, emphasizes, biases and/or gates affirmative and/ornon-affirmative artifacts. As such, the system associates a characterand/or semantic identity with high entropy role goals (e.g. in rapportwith a leadership/principal role and/or an overall (mission)strategic/high-level goal and/or message) and further biases it withartifacts (e.g. accents) and/or behaviors associated with inversepolarity resonant artifacts in the target semantic group (e.g. audience)thus, further emphasizing the entropy, drift and/or polarity between theoverall goal/message and the inverse character goals and/or behaviors.

Further it may cause increasing the resonance with the target (semanticgroups) audience and further factorization associated with the overallimpression/rating (e.g. factorize the affirmative factors and/orresonance by increasing the entropy between the mission (e.g.advertising/presentation/movie goals/message) and the non-affirmativeresonant artifacts associated with the inferences related to theemphasizing role character; in other examples the system biases a friendcharacter with affirmative resonant artifacts.

The system may generate new compositions and/or further missions byfactorizing semantic artifacts based on fluency factors (goals).

The compositions may comprise documents, images, videos, overlays,sounds, tactile, multimedia artifacts, presentations, semantic wave, webpages, postings and/or any other artifacts which may be generated bysemantic augmentation. Further, the mission of such compositions may berelated with advertisement, artistic, health, diagnosis, communication,teaching/learning, entertainment and/or further augmentation.

The system may use and/or generate compositions with and/or betweenartifacts (e.g. compose two generated multimedia artifacts, two videos,a video and a sound stream, a sound and post motion, an overlay and apost motion, two overlays etc.). In some examples, the system composestwo optical channels and/or video streams. In further examples, thesystem composes streams and/or semantic waves from at least two devicesand/or communication channels (e.g. two mobile phones, sound and/orvideo, two communication channels with different radio/network protocolsetc.).

In some examples, the system applies a bias to the emphasizing rolecharacter. Further, the bias may be goal oriented, composite and/orsemantic time dependent (e.g. affirmatively emphasizing ornon-affirmatively emphasizing based on particular goals, semantic timeand/or further biases).

In further examples, the system starts a new presentation, teachingsession and/or composition comprising recorded and/or augmentedsnippets. As such, the system visualizes a situation which must berecorded based on the presentation and/or trip goals and/or furthershares it in a semantic resonant group. The system acts (e.g. recordsartifacts and/or further explanations, actuate etc.) based on variableentropy between the goals and/or happenings in the semantic field.

In further examples, the system generates renderings of shape designs,outfits, components, modules, posts, gears, maps, mission briefs andfurther augmentation artifacts.

In case that the entropy, shift and/or drift between the goals and thesemantic inference is high then the system may undertake high entropy,shift and/or drift actions from the intrinsic behavior (e.g. generatealarms, spray halocarbons etc.).

The system expresses opinions and/or perform semantic augmentation basedon high entropy reverse polarity analysis. The bias, polarity and/orpolarization of such opinions may be further inferred and used in thesemantic (publishing) chain.

In further examples, of semantic augmentation for generating and/orpresenting a rendering, presentation, document, movie, email, courseetc. the system may create various paragraphs, sections, snippets,frames, and/or images in such a way that while slightly preserving thecoherency of strategic goals and/or message it may create highersuperposition and/or confusion in order to allow reading and/orcollaborating parties to further increase semantic spread (for)resonance, reduce confusion based on own model and/or encouragechallenges. Further, the system may create borderline resonances withinpresentation/rendering of semantic identities, groupings, positionings,colorings, textures and/or further artifacts.

One of the system's strategic goals when generating, presenting and/orteaching is to preserve fluency factors and/or indicators within aspecific interval and/or further within a semantic time (interval). Insome examples, the interval is a semantic interval.

The system may plan and/or selects semantic artifacts based on projectedfluency.

The system may highlight, select and/or overlay various images,paragraphs, snippets and/or other artifacts based on the resonanceand/or further polarity between various collaborators, groups, owners,presentation attendees, users and/or further artifacts.

It is to be understood that the friends and/or foe inference and/orartifacts may be associated with semantics such as PAL, FRIEND and/orsimilar (synonym, low shift/drift/entropy, resonant etc.) and may beapplicable wherever they occur in the current application.

The system may infer semantic inference rules, routes and/or furtherartifacts based on ingesting the current patent application and/orfurther continuations.

The system may generate document content and associated tags based onsemantic analysis (e.g. emails, html, postscript etc.).

In the case of semantic identification (collapse) the system maydetermine leadership artifacts in rapport with and object, artifactand/or semantic identity which is not associated, do not match and/or donot collapse to other particular semantic identities and thus the systemmay not associate such (other) semantic identities to the object and/orartifact.

In examples, some semantic views, streams and/or fluxes may behierarchically generated and/or rendered based on the requiredresolution and/or coverage. In some examples, such generating and/orrendering may be based on semantic wave and/or wavelet compression.Further, the system may analyze such renderings and/or artifacts atdifferent resolutions based on deep learning and further semanticanalysis.

The system may seek inference within semantic resonance operatingpoints/intervals to identify, pursue and/or render goals, positioning,location, routes, group, rules, user interfaces, components, graphs,actuation, commands etc. Further, systems may perform negotiation and/orsemantic flux inference/challenge based on tuning the semantic spread tooperate within a resonance operating point/interval with a collaborativeand/or negotiation partner. In some examples, the system assesses theresonant and/or non-resonant capabilities and/or semantic artifacts todetermine and/or guide inferences, goals, behaviors and/or projections.In case that the system determines non-resonant artifacts, it mayfurther increase the semantic spread and learn/determine routes and/orrules which achieve positive sentiments/polarity in rapport withnon-resonant artifacts. Further, the system may learn negotiation skillscomprising operating goals, groups, routes and/or rules which determinelower risk, factorizations and/or higher (entanglement) (entropy)factors, in rapport with non-resonant artifacts.

The system may infer leadership based on negotiation skills.

In further examples, leaders may be promoted and/or use such negotiationskills to achieve particular goals.

Semantic groups may be inferred based on resonance with artifacts havingnegotiation skills. In some examples, the system infers semantic groupsfor trading and/or negotiating securities, rates, budgets, risks and/orother indicators.

The system may use leadership inference and/or resonance for determiningpreferred brokerages and/or insurers. In some examples, a leader withina group is deemed as a broker and/or insurer in particularcircumstances. In further examples, resonant entities are deemed asbrokers and/or insurers in particular circumstances (e.g. based onsemantic time).

Within this application the term “influence”, “influencer” and/orrelated terms may be understood as artifacts pursuing semanticresonance. Further, the semantic resonance may be achieved through avariety of skills including leadership, teaching, negotiation,influence, polarization among fluxes and/or semantic groups etc.

As mentioned, the system replenishes stocked articles based on semanticinference.

The system uses semantic analysis to keep optimal stocks, optimalavailable budgets, publish semantics and/or costs.

The system uses resonance inference to infer optimality. In someexamples the system performs semantic publishing based on optimality ofloss and/or gain.

The semantic route inference may get blocked, halted, expired and/orinvalidated; further, the system may use semantic trails and/or furtherinference to learn why the inference was blocked, halted, expired and/orinvalidated. The blocked, halted, expired and/or invalidated inferencemay use partial semantic budgets and thus, the feedback/explanation ofpartial inferences may allow the system to learn new semantic artifactsbased on feedback and/or the consumed semantic budgets. In some cases,the system does not expire the blocked and/or halted inference, insteadwaiting to proceed when the semantic time allows, potentially withupdated budgets.

Techniques such as explained in this application may be used in missionmanagement wherein the system assigns likings, optimizations,preferences and/or goals comprising semantic budgets and thus, thesystem may pursue the mission by inference on such guidelines whileblocking, routing, re-prioritizing, re-budgeting and/or invalidating thegoals when the inference for such goals gets blocked, halted,non-resonant, expired and/or invalidated.

In further examples, the semantic artifacts are embedded in documents(e.g. html, PDF, word, excel, power-point etc.), potentially within(tagged and/or delimited) fields, paragraphs and/or sections. It isunderstood that the embeddings may be specified in terms of challenges,semantic identities, inference augmentation (e.g. textual, ui controls,sensing/actuation/signal etc.) and/or explanations (e.g. of why asemantic artifact couldn't be achieved, why is blocked, risks ofbudgeting etc.).

In distributed inferences, if a route inference at system A challengedsystem B for a semantic within a budget and the inference at B getsblocked then the system B may stop the inference and report to thesystem A why the inference is stopped and/or is blocked. Further, theinference at system A may decide to further challenge B, use (alternate)semantic routes and/or indicate to B to forget and/or invalidate any ofthe challenges and/or associated artifacts. It is to be understood, thatthe challenges at B may use partial semantic budgets and at such thefeedback/explanation from B to A may allow A to learn new semanticartifacts based on feedback and the consumed semantic budgets.

A and B may explain to each other the meaning of signals, inputs and/oroutputs; such explanatory interfaces may be used by the learner to learnand/or generate semantic artifacts including semantic rules (e.g. timemanagement, access control, factorization etc.). In cases where theconfusion is elevated during the ingestion and/or inference of theexplanation process the confused system may further challenge theexplainer, fluxes and/or artifacts for reducing the confusion. Insimilar ways, the system may proceed with ingestion and/or inference ofexplanations for particular ratings, risks, factors, indicators and/orfurther semantic artifacts.

An explanatory system, interface and/or challenges may be used todescribe the rules, signals and/or eventual consequences.

In some examples, the teacher may detect confusion and/or a low level(hierarchical) understanding in learner and as such uses furtherchallenges to reduce confusion and/or teach learner higher levelexplanations and/or associations.

When the system teaches and/or is taught it may comprise goals such asachieving semantic resonance in relation with the teaching goals.

The system's learning and/or teaching goals may progress throughsub-goals wherein the sub-goals are progressing from general knowledge,transfer knowledge, abstract knowledge to specialized knowledge relatedto the goal.

While the teaching and/or learning may be by example, alternatively, orin addition, the system may learn by challenges.

The explainer (e.g. teacher) may provide an explanation by examplewherein exemplification of past and/or resonant experiences are streamedto the learner. It is to be understood that the explanation may comprisesemantic artifacts and/or further multimedia artifacts (e.g.images/frames, video clips, audio clips, wavelets, semantic waves etc.).

The teacher may provide explanations which resemble and/or resonate atthe learner with past, current and/or projected semantic artifacts.Further, the learner may use those resonances for semantic analysis,learning, rendering, action and/or further challenges.

Approval factors may be inferred based on resonances and/or furtherelevated fluency (of semantic identities) in rapport with leadershipgoals.

A system may perform semantic learning (e.g. recording/learning semanticartifacts), indexing and/or biasing based on elevated fluency andfurther approval factors.

Teaching factors and/or indicators may be associated with semanticidentities and semantic groups wherein the teaching factors and/orindicators are associated with operating in a resonance and/or resonant(semantic) interval while increasing the semantic spread. In someexamples, the system uses particular themes, drives and/or profiles inorder to perform teaching.

The teaching may be based on challenges between teacher and student. Infurther examples, when the student challenges the teacher, the teachermay provide clues and/or further challenging of the student. The teachermay use a plurality of challenges and/or responses to induce coherentinferences at student while the student infers and invalidates(eliminates) non-sensical inferences and/or associated artifacts.

The teacher and/or learner may further create resonance in one anotherby inferring and/or using semantic attributes, biases and/or furtheradjusting artifacts for achieving resonant challenges. In an example,they may adjust the pitch, timbre, volume, the pace, the resolution, thefont size, colors and/or accent in the augmentation (e.g. sound,display, tactile etc.) in order to resonate with the collaborator; it isto be understood that such adjustments may be based on the collaboratorssemantic profiles and/or further previously inferred semantic attributesabout the collaborator (e.g. from direct challenges from thecollaborator, from multimedia, other streams/fluxes etc.).

Learning/teaching biases towards a semantic identity (e.g. teacher,learner, learning group, teaching group etc.) may be used and/orfactorized based on semantic artifacts inducing affirmative resonance inlearner/teacher and/or groups thereof. In some examples, the system mayavoid generating non-affirmative inferences in a collaborator (teacherand/or student). In further examples, the system may avoid challengesgenerating non resonant inferences in rapport with the collaborator.

The system performs analysis on the movement of a semantic identityand/or semantic group. In some examples, such analysis is used toperform teaching, movement correction and/or learning. In a furtherexample the system uses semantic trails, routing, shifts/drifts and/ororientation of detected movement in comparison with goals, guidelinesand/or examples (describing and/or depicting the movements). Theguidelines refer to artifacts in the semantic field from sensing,multimedia, video, frames, fluxes, streams etc.; in some examples thesystem specifies that the goals and/or inferred guidelines should beassociated with FOLLOW JOHN'S PITCH MOVEMENT BUT DON'T LEAN THAT MUCH ORLEAN LESS and as such the system perform semantic analysis (e.g. thedrift, shift, orientation and/or entropy based on such routes, rules,guidelines and/or trajectories inferred) from a first (e.g. of John's)and a second (e.g. of the learner) set of multimedia, video and/orstream artifacts. Further, the system provides semantic augmentation inthe form of challenges based on whether the learner achieved and/or notachieved the routes and/or goals (e.g. get a notification/warning thatit has LEANED MORE THAN JOHN, a notification/praise that it has LEANEDLESS etc.). It is to be observed that the warning may be based on aresult which comprises the orientation, entropy and/or shift of the/adesired behavior (LESS vs MORE, JUST RIGHT etc.).

The system may project inferences (e.g. of drive semantics) which areoffensively factorized (e.g. high risk), have high entropy in rapportwith known knowledge in a particular domains and further factorizing it,smoothing it and/or applying it for another domain.

In further examples, the system has goals to increase the number ofborderline resonances while preserving coherence and confusionreasonable (e.g. within a resonant and/or friendly interval).

In some examples, the system determines coherent, not confusing and/orresonant artifacts based on inferences related to artifacts associatedwith at least two endpoints and/or hierarchical levels and thus performssemantic learning based on such resonances.

The system may project that particular semantic routes may not achieveresonance with a particular artifact and/or collaborator within asemantic time interval and as such it may use alternate semantic routes,semantic time and/or projections to achieve the resonance goal. It is tobe understood that the system may determine that the resonance may onlybe possible by challenging the collaborator and causing it to change itsmodel to be resonant with the goal. Further, by challenges to furthercollaborators the system may change the circumstances, model and/orresonance artifacts in the collaborator.

In order to achieve semantic resonance the system may challenge and/ordiffuse to an entity with projections which are non-resonant,non-affirmative resonant and/or hardly believable with the collaboratorin order to increase the semantic spread operating interval at thecollaborator in regards with the goal at hand. At a later semantic time,the system may challenge and/or diffuse increased (e.g. byfactorization, indexing, hysteresis, damping etc.) resonant inferencesat collaborator and thus increasing likeability factors. In similarways, for increasing likeability of a semantic artifact (e.g. related toa semantic identity, activity, cost/budget, option, goal etc.) thesystem may challenge and/or diffuse various projections from which someare non-resonant, hardly resonant, negatively factorized and/orassociated with higher dissatisfaction factors while others are moreresonant, less negatively factorized and/or associated with lowerdissatisfaction factors and thus increasing the factorization of thelikeability factors for the latter projections, options and/or routes.Further, the association of semantic identities with more likeableand/or less dissatisfactory options while dissociating the semanticidentities with the less likeable and/or more dissatisfactory optionsmay increase the popularity related with the semantic identity andcreate a resonance with such semantic identity. In an example of anexplanatory system, a vehicle display unit is coupled to an analogand/or digital speedometer; the speedometer may send signals to thedisplay unit which are not understood at first by the display unit.However, an explanatory semantic unit may be coupled to the wiring linkbetween the display unit and the speedometer and further be configuredto explain and/or translate to the display unit the signal. Asexplained, the semantic unit may comprise semantic flux and/or streaminterfaces and further be semantically configured via fluxes, I/Osystems and/or other interfaces. In some examples, the semantic fluxfrom the speedometer manufacturer is challenged in regard to the meaningof speedometer inputs before being displayed on the semantic unit. It isto be understood that the explanation may be based on the voltage and/orcurrent provided by the speedometer and/or further semantic indexingfactors (e.g. 3V is no speed or intrinsic behavior, +0.10V is +1 km/h,0.16V is +1 mph etc.). Further hysteresis and/or damping factors may beexplained for improved accuracy and/or interpretability. Suchexplanations and/or challenges may be provided by inputs, semantics,multimedia artifacts and/or other modalities as explained in theapplication.

Further the system infers, renders and/or display semantic artifactsassociated with the explained semantics (e.g. speed) and performsemantic augmentation. In some examples the system renders semanticartifacts on displays based on particular semantic profiles. Further,the user may specify how those displays and/or controls should berendered. In an example, the user may prefer various colors and/orindications for the speedometer pointers and/or speed ranges (e.g. BLUEFOR HIGH SPEED, RED FOR LOW SPEED, GREEN FOR RECOMMENDED SPEED); it isto be understood that the system may infer those based on other relatedinferred artifacts which resonate with the current user's (leadership)circumstances.

The system may infer, adjust and/or factorize likeability, preference,satisfaction, trust, leisure and/or affirmative factors based on high(entanglement) entropy inference in rapport with (higher)dissatisfaction, concern and/or stress artifacts and vice-versa.

While the preference indicators might be used to favorably factorizesemantic artifacts in rapport with an entity it is to be understood thatthe system may use inconsistency analysis to establish risk of thepreference indicators and/or factors. In an example, the system may haveset a high PREFERRED factor for buying CAMPING articles from a providerbut because the experience (e.g. comprising expectations and/orconsequences goals) is inconsistent (e.g. sometimes affirmative,non-affirmative and/or not in an operating interval) the system maychange the preferred factors to reflect inconsistency (e.g. via risk,inconsistency and/or reliability indicators/factors) and/or infer timemanagement rules about inconsistency (when the provider is preferred,consistent, less risky, induces affirmative/non-affirmative resonanceand/or when it is not (or having high entanglement entropy)).

A semantic view frame may be represented as a semantic group and thesystem continuously adjusts the semantic factors of semantics, groups,objects and scenes.

Semantic resonance related with lower risk to reward factor maydetermine offensive behaviors. Analogously, semantic resonance relatedwith higher risk to reward may determine defensive behaviors.

The system may focus resources, sensing and/or elements based onsatisfaction, trust, leisure, affirmative, dissatisfaction, concernand/or stress factors. As such, the inferences in particular semantic(frame) views may be assigned more budgets (e.g. by indexing, damping,hysteresis etc.) based on such factor intervals. In an example, thesystem determines an operating interval (e.g. based on resonance)wherein the cyclist scene movements and/or features induces coherentaffirmative inferences. It is to be understood that the system elevatesto leadership such resonant movements and/or features and furtherfactorize them based on semantic profiles; in some examples, inducedresonance by observing cyclist eyes and/or facial expression may bearmore leadership.

The system may focus on a scene and/or view by adjusting (e.g. decrease)the semantic spread. In some examples the system considers, selects,gates, allows and/or diffuses only semantic coherent and/or resonantartifacts associated with the scene's and/or (semantic) view'sartifacts, leaders and/or drives.

In further examples, the system uses a discomfort/comfort factor and/orindicator to determine and/or control the behavior of the system inrelation with the observability in the semantic field; as such, therendered, sensing and/or augmentation elements may behave (e.g. steer,move, vibrate, speak etc.) in a way which is uncomfortable/comfortablefactorized. In some examples, the vibration and/or pitch is too high,too long, too short and/or within an inappropriate semantic time; thecamera sensor moves (e.g. too fast or too slow) and/or zoom in an on/offfashion within an interval of (semantic) time; camera keeps observingfor too long; the gaze of a rendered and/or augmented face is staringfor too long, too short and/or in an intermittent fashion at the useretc.

Increase in (aggregate) resonances may determine increase in stimulationfactors and/or stimulation circumstances.

When the stimulation factor is high and/or the number of resonances highthen the confusion and/or superposition factors may increase. The systemmay reduce the confusion factor by reducing the number of resonances byinvalidating and/or conditioning the resonant signals and/or groups.

The system goals may be based and/or associated with increasingstimulation circumstances. In some examples, increased offensive and/ordefensive behaviors may be associated with increased stimulationcircumstances and/or factors. Thus, the system may perform semanticsmoothing for optimizing (e.g. damping, keeping in a hysteresis intervaletc.) the moods determined by stimulation circumstances.

In further examples, the system factorizes stimulation factors based oncomfort/discomfort factors and further offensive/defensive behaviors. Insome examples for offensive behaviors the stimulation is positivelyfactorized for increased comfort and/or, potentially based on high(entanglement) entropy, negatively factorized for increased discomfort;analogously, based on high (entanglement) entropy the stimulation isnegatively factorized for decreased comfort and/or positively factorizedfor decreased discomfort. By H/ENT of offensive/defensive behaviors, thestimulation is negatively factorized for increased comfort and/or,potentially based on high (entanglement) entropy, positively factorizedfor increased discomfort; analogously, based on high (entanglement)entropy the stimulation is positively factorized for decreased comfortand/or negatively factorized for decreased discomfort.

The system may adjust stimulation factors and/or furtheroffensive/defensive behaviors in order to avoid overstimulation (e.g.sensory and/or information overload, high confusion etc.) and/orunder-stimulation. While overstimulation and/or under-stimulation may beseen as entropic artifacts when related/entangled to a semantic identityand/or constraint it is to be understood that they may not be entropicwhen are not entangled and/or assigned to a semantic identity and/orconstraint. In some examples, an entity may be overstimulated in regardto particular artifacts and/or fluxes and/or under-stimulated in regardto others.

The system may adopt a more defensive behavior when is challenged bynon-affirmative factorization challenges and/or collaborators (e.g.inducing less resonant, non-affirmative resonant, non-resonant, higherrisk to reward factor, negative polarity inferences and/or anycombination thereof). Analogously, the system may adopt a more offensivebehavior when is challenged by positive factorization challenges and/orcollaborators (e.g. inducing higher resonant, affirmative resonant,lower risk to reward factor, positive polarity inferences and/or anycombination thereof). It is to be understood that the negativefactors/factorizations for particular artifact/s indicators and/orsemantic groups thereof have and/or determine high (entangled) entropyvalues of the corresponding positive factors/factorization for the sameartifact/s and vice-versa.

The system may become more defensive towards the inferences related to achallenger when the challenges it receives determine non-affirmativeresonances and/or high entropy inferences in regards with core semantictrails and/or routes.

Semantic resonance related with lower risk to reward factor maydetermine offensive behaviors. Analogously, semantic resonance relatedwith higher risk to reward may determine defensive behaviors.

The semantic resonance may be used in relation with signal analysisand/or semantic profiles. In an example, the system detects and/orgenerates signals, semantics and/or semantic waves which are resonantwith particular semantic profiles; further, the resonance operatinginterval is determined and/or learned based on such profiles.

The system may project propagations and/or diffusion of shapes,compositions and/or markers.

The system may learn semantic rules and routes as resonance occurs atthe same endpoint and/or semantic time. Further, the resonant artifactsmay be considered as entangled until expiration, invalidation and/ordecoherence occurs.

Semantic analysis comprises semantic composition, semantic fusion,semantic routing, semantic resonance, semantic indexing, semanticgrouping, semantic time and/or other language based semantic techniques.

Semantic leadership is inferred and/or adjusted based on semanticanalysis including semantic factorization.

In some examples, the system performs semantic inference whilemaintaining a trail of semantic artifacts and/or leadership which havebeen considered during inference. As such, the system can performsemantic learning of cause-effect, biases, anomalies and/or furtherinferences.

The system may use variable coherent inferences based on at least onecoherence/incoherence indicator and/or factor. In some examples, thesemantic analysis uses such factors to assess the coherency/incoherencyof the inferences. It is to be understood that the coherent and/orincoherent inferences may be based on high (entanglement) entropy ofcoherent vs. incoherent.

The semantic posts comprise at least one component allowing the superiorand/or inferior parts of a post and/or module to move in particular ways(e.g. tilting, flexing, moving sideways etc.). In some examples, thecomponent comprises shafts supported by bearings and/or bushings whichallow the module's parts to tilt longitudinally to the axis of theshaft. Thus, the tilting mechanism allows the move and tilt towardseach-other and further connect by using any of the previously explainedcoupling methods.

In some examples, the flexing mechanism allows the adjustment of thecomposite base in which the inferior part of posts adjust and/or aretilted sideways for increasing the base of support and/or adjusting thecenter of pressure or zero moment point inside the base of support (e.g.towards a centered semantic zone and/or endpoint etc.). It is to beunderstood that the base of support may be modeled and/or mapped withinthe semantic network model and the system adjusts the center of pressureand/or zero moment point in the base of support within semantic networkmodel based on semantic inference and/or analysis. Analogously, thesystems adjust the composite plate carrier wherein the superior part ofthe posts are adjusted, moved, shifted and/or tilted sideways foradjusting the center of pressure or zero moment point inside the base ofsupport.

In further examples, the posts include a swiveling arm and/or barrierwhich is connected to a/the hook, latch and/or gripper. In retractedposition the swiveling arm is parallel and/or side by side with the postwhile being attached to the post in at least two regions, a superior onewhich is attached to the hook/latch/gripper and an inferior one which isattached to a module which comprises a motion controlling mechanism. Ina preferred embodiment the motion controlling mechanism comprises atleast one actuating shaft, lug, inner tube, arm etc. and furthersupports (e.g. bearings, bushings, lugs, nuts etc.). The actuatingshaft/arm is controlled via on-board/on-module motors and allows thesideway extension of the arm and hence the hook/latch/gripper. It is tobe understood that the sideway extension may comprise lateral, vertical,angled movement and/or extension of the arm such as the upper portionsupporting the hook/latch/gripper stays at the same height duringextension. The arm itself may comprise inner tubes and/or componentswhich extend and/or collapse to optimize hooking/latching/gripping atvarious heights and/or configurations. Further, the arm module maycomprise a circular swiveling mechanism/platform which allows the arm toswivel in a rotating manner; the rotating swiveling mechanism maycomprise rotating shafts, toothed wheels, bearings, bushes and/or othercomponents in order to transfer torque and motion from the motor.

In order to adjust to the opposing forces generated by pulling and/orpushing (e.g. other posts, carriers etc.) the posts, modules, wheels,suspension and/or swiveling arms may move in order to adjust the centerof gravity, gravity line and/or base of support. In some examples, atleast one post (or group of posts) need to trail, tow and/or drag atarget load (e.g. another post/s and/or groups thereof). As such theswiveling arm moves towards clipping and/or clamping with the targetload (e.g. by hooks, clipping, clamping and/or gripping mechanismsetc.). In order to preserve the stability endpoint and optimize thecenter of gravity and the base of support the system adjusts the load onthe wheels (e.g. retracts the wheels farther away from the load and/orlift the wheels closer to the load); as such, the system may know thatis in the TRAIL, TOW, LIFT and/or other similar/synonym modes and thus,when it senses additional load on particular wheels, inertial movementtowards outside of the stability endpoint and/or towards the instabilityendpoints and/or areas it may adjust the wheel lift, damping, movementand/or braking (e.g. based on semantic indexing, damping etc.). Further,additional movement and/or semantic shaping of the arm may generateadditional towing moment in particular situations (e.g. once connectedto the target load moving the arm to point toward the direction ofmovement). Further, if the target load comprises semantic posts, unitsand/or modules the trailer may coordinate the operations with the targetunits; as such, the target units may position and/or adjusts arms, lift,damping, movement and/or braking in order to allow achievement of the(entangled) composite system goal. It is to be observed that the twosystems (towing and target) may be considered entangled in regards tothe tactical goals (e.g. maintaining stability) and/or furtherconstraints as the change in one system posture and/or connection mayaffect the other system stability and/or the entangled system stability.

The system may further attach modules and/or components to other assetswhich require mobility and/or need to move from one endpoint to another.

In some examples, the system attaches at least a semantic unit, a post,a mobility module and/or a mobility base to a chair, bench and/or othersitting element. In further examples, post's components (e.g. grips,hooks and/or latches) hook and/or latch into the base and/or legs of thechair. In addition, the system deploys the posts based on inferencesrelated to the ensemble's center of gravity, base of support and/orfurther requirements and/or capabilities (e.g. damping, elevation,weight, width, noise etc.).

As presented, the mobility asset module may attach and/or be attached tothe base of the chair via a mobility base which comprises a (lockable)groove and/or channel where the chair legs can be inserted and/orlocked.

In other examples, the mobility moving module and/or mobility basecomprise grippers which grip the chair legs.

The mobility asset modules may be coupled together through a commonframe and/or (groups) of posts. Further, the modules may incorporate asupporting base for the asset components; it is to be understood thatthe supporting base may ensure that the asset is elevated to allow themovement of the mobility base.

The mobility asset modules and/or their components may comprise and/orbe connected to sensors. They may include weight sensors, (multipleaxes) accelerometers, gyroscopes, magnetometers, cameras and/or othersensors. These sensors may be used to detect and/or adjust themobility/asset ensemble center of gravity and/or further base ofsupport. In some examples, the sensors are connected to the lockablebase and/or grippers.

A mobility module may incorporate a gripper and a retractable supportingbase. The gripper may lift the asset component (e.g. chair leg) whilethe retractable supporting base extends, slides and/or locks under theasset component in order to provide support while moving. Duringunloading in position, the retractable supporting base retracts and/orunlocks while the gripper lowers the asset into position. It is to beunderstood that the gripper and/or retractable supporting base may becomprised in the same module or different modules.

In an example, a mobility group comprising mobility modules is taskedwith moving a chair from location A to location B in a specificorientation; as such, the mobility modules move into position at each ofthe chair's supporting legs, grip/lock/latch to them, lift the chair andfurther sliding the support bases under each chair's legs and/or furthergripping and/or locking them; once the asset is secured in place (e.g.at all the legs) the mobility modules start moving the asset from A to Bas per goal. Once the asset is in the desired orientation, positionand/or location B the supporting base may retract, the asset is loweredand/or further the locking, latching and/or gripping is released.

A variety of other options may be used for moving bases and/orattachments. In an example, the moving modules latch, lock and/or gripeach to the chair's legs; alternatively, or in addition, they may latch,lock and/or grip the chair frame, sitting area and/or other component.In some examples, the chair comprises latching, locking and/or hooksallowing easier interconnection. It is to be understood that the movingmodules may be coupled and/lock together and/or to a further moving,lifting/lowering and/or locking base.

In similar ways with the lockable barriers, a semantic post mayincorporate a movable arm which allow the bands and/or lockingmechanisms to reach and connect to each-other. As such, the arm may bealigned vertically along the post in an initial intrinsic position. Itstip may comprise the connector and/or locking mechanism which extendand/or guide the bands, dividers and/or meshes. The arms move from thevertical position towards a more angled and/or horizontal position untilthey reach each-other (e.g. with another post arm) and are able toconnect and/or lock the bands, dividers and/or meshes in place. Onceconnected the posts may further maneuver to extend the connected bands,dividers and/or meshes; further, once connected, the system may or maynot retract the arms in the intrinsic position.

A post may possess at least one arm, lock, band and/or hook. Further,the post may possess multiple arms—e.g. one for guiding and/or locking asuperior band, part of a divider and/or mesh and one for guiding and/orlocking an inferior band, part of the divider and/or mesh.

In further examples, the system takes into consideration theenvironmental factors (e.g. temperature, wind, noise, pollution etc.).In cases when wind is present the system may index the damping,positioning, orientation and/or stance of the modules, arms, fasteningpoints (e.g. latches, hooks etc.), rows and/or columns.

The system may deploy and/or guide mobile post enabled assets based onsemantic augmentation. Further, the user may indicate where, how and whythe assets are deployed. In some examples the user indicates zonesand/or trajectories where the assets to be deployed. In further examplesthe system is instructed to deploy the assets for a HEALTHCARECONFERENCE WITH 300 ATTENDEES configuration and thus the system infersthe optimal endpoints for each assets based on circumstances (e.g. basedon the availability of conference rooms and characteristics; ensureproper distancing during a pandemic; being healthcare related ensurereasonable high networking, learning and/or friendliness etc.).

The assets (e.g. chairs, posts, sensors—microphones, cameras, antennasetc.) may be optimally oriented in such a way that they serve thepurpose of creating optimal resonance within/between (the leadership)(entangled) semantic groups (e.g. attendee/user, speaker) increasedstimulation and/or satisfaction; thus, the chair/platform/post/sensor isoriented toward endpoints and/or locations creating maximum resonanceand/or stimulation (e.g. the speaker platform is oriented toward thesemantic group of attendees/users and/or associated endpoints, the userplatform is oriented towards the speaker platform or towards analternate augmentation method based on its semantic profile and/orbehavior etc.). It is to be observed that the sensing (resonant)orientation from an attendee/user to a speaker and from speaker toattendee/user may facilitate elevated (projected) resonance despite thembeing in mainly opposite directions; this is to be understood that sincethey represent an entangled mission (e.g. “ensure resonance betweenattendee and speaker”) and/or (projected) further (entangled) group thesystem may look to optimize (projected) entanglement and/or resonance bydiffusing and/or intersecting resonant areas and/or trajectories.

The posts movement may be mapped to various hierarchies in the semanticnetwork model. In some examples, the system may infer artifacts (e.g.endpoints, routes etc.) where the resonance, friendliness and/or furtherstability is elevated as opposed to other options; as such, the systemfactorizes the positive polarity of safety in regards with suchartifacts. In further examples (e.g. when the system is in offensivemode), it may look for artifacts and/or areas projecting stimulationand/or motivation (e.g. when in defensive mode).

The system calculates and/or project the movement of the sensor platformusing available sensing and further uses it to index and/or bias theposition of artifacts in the semantic field and/or infer furthersemantic artifacts. In some examples the system compensates for themovement of a moving module, post, carrier and/or vehicular platform.Alternatively, or in addition, the system compensates for any sensingand/or damping of mountings, support and/or casing movements which maybe installed on the platform. While such compensation may occur, it isto be understood that this may be coupled with indexing and/or biasingof damping, torqueing and/or stabilization techniques for platformand/or other supporting components. Thus, the system project semanticroutes comprising such indexing and hence when the motion occurs thesystem uses the indexed and/or biased semantic artifacts for inferencewhile decreasing the shift, drift and/or entropy of future inferences.

The robotic systems may be out of budgets (e.g. energy etc.) and as suchthey may need to be stored in a safe location (e.g. allowing charging,non-impeding, secure etc.). As such, the system determines safe and/orfriendly zones and ensures safety budgets and/or associated hysteresisneeded to reach the safety. The safety budgets and/or hysteresis may bemore elevated when the confusion/incoherency is high and/or based onparticular factors (e.g. risk high, budget gain low etc.).

We explained the use of rules and/or routes associated withdo/allow/preferred/recommended/follow (and/or related synonyms) andtheir high (entangled) entropy (and/or antonyms) “donot”/block/not-recommended routes, rules and/or guidelines. Sometimes,the system infers “blocked” semantics related to contextual artifactsand/or inferences. In an example, the system may infer that acontractual document associated with particular semantic artifacts mayimpede and/or block a contracting party from performing certain actionswithout potential consequences. Analogously, the system may determinethat a contractual clause may encourage, allow and/or diffuse aparticular behavior.

The system may infer a leverage factor/indicator related with particularsemantic artifacts, goals and/or inferences. The leverage factor mayindicate the (composite) leverage that the system, user, operator,group, semantic identity and/or any other semantic artifact has inrapport with current and/or projected artifacts and/or inferences. In anexample, the operator may want to develop a new product in the area offinancial markets and thus the system analyses the leverage based onadvantages and/or disadvantages that the operator has in rapport withgoals, resonant and/or non-resonant semantic identities (e.g.competitors/foes, partners/friends etc.) and/or other artifacts;further, the advantage and/or disadvantage analysis may comprise blockand/or hardly diffusive type of rules for advantageous capabilitieswhich may impede/hardly-diffuse/block competition and/or relatedsemantic groups (e.g. non-affirmative resonant) and/or block and/orhardly diffusive type of rules for disadvantageous capabilities whichmay impede/hardly-diffuse/block self and/or related semantic groups(e.g. diffused, affirmative resonant etc.) from competing. The systemmay determine allow/easy-diffuse types of rules for unimpededcapabilities of self, friends and/or foes. It is to be understood thatthe allow/easy-diffuse and/or block/hardly-diffuse may be based on high(entanglement) entropy inference.

The system may use polarity and/or further polarization analysis toidentify and/or generate performance sentiments regarding markets, capstocks, seasonals, domains, leadership boards, people etc. In furtherexamples, based on such analysis the system may generate news comprisingopinions. Alternatively, or in addition, friend/foe and further semanticanalysis may be used.

The system may consider competition participants such as entropicentities (e.g. FRIEND 51% AND FOE 49%; FRIEND 50%, FOE 50% etc.).Further, based on the degree of entropy the system may determine neutralbehaviors (e.g. FRIEND 50%, FOE 50%, NEUTRAL 100%) towards such semanticidentities.

As mentioned in the application, the factorization of leadership(semantics) may impact semantic budget and/or semantic spreads. In someexamples, the lesser (or higher) factorized the resonant leadership is,the lesser (or higher) the discriminatory artifacts, indicators and/orcapabilities are. The system may look to attain semantic identitiesother than of self with higher (or lower) factorized leaderships (e.g.based on groupings leadership, leader (composite) semantic group etc.).In some examples, the system may use higher leaderships when the budgetsare low and/or tight wherein (the possible and/or allowable shift,drift, entropy and/or hysteresis is low); analogously, the system mayuse lower leadership when the budgets are high and/or not tight. It isto be understood that the system may decrease its capabilitiesleadership when increasing other semantic identities leadership;analogously, it may increase its capabilities leadership when decreasingother semantic identities leadership.

The system may receive feedback associated with affirmative resonant ornon-affirmative resonant artifacts. When the affirmative resonantartifact feedback is negative then the system may index its factors morethan when receiving feedback from non-affirmative resonant artifacts.

The system may bias anchoring based on projected inferences. In someexample the anchoring is based on semantic trails comprising previouslybudgeted securities and/or endpoints. The anchoring may be based on lesshazardous and/or more safe endpoints in defensive behaviors; by H/ENTmay be based on more hazardous and/or less safe endpoints and/orartifacts in offensive behaviors.

The system may use projections based on behavior analysis of(affirmative and/or non-affirmative) resonant and/or non-resonantplayers to bias the anchors.

The system uses semantic trails and/or routes to determine, infer and/orproject advantages and/or disadvantages comprising mapping and/orlocalization at an endpoint. Further, the system may determine theadvantages and/or disadvantages of the transitions from an endpoint toanother endpoint by composing the advantages and disadvantages based onsemantic inferences.

In some examples the advantages are associated with friendliness whilethe disadvantages with foes.

The system performs drift analysis between the optimal and/or desiredtrajectory of securities (e.g. stock, market indices, ETF, budgets,personal and/or group safety etc.) between two points in time and theactual trajectory (e.g. FIG. 18 ). As such, when the difference betweenthe desired and actual value is high at an endpoint based mapping, thesystem may learn a semantic rule associating the leadership semantics(e.g. volume, employment statistics, natural disasters, hazard etc.) asinferred in the semantic field with an indexing rule associated withhysteresis and/or damping factors.

In further examples, the system learns leading indicators by groupingand/or compositing leadership semantics in the semantic routes and/ortrails which generated particular outcomes (e.g. (strategic) goals forstronger economy etc.). In some examples the outcomes are related withsemantic analysis on lagging economic indicators and/or groups thereof(e.g. unemployment rate, CPI, interests, deficits etc.).

In some examples the indicators are inferred by the system and thesystem's goals is of achieving particular factorizations for suchindicators in (semantic) time intervals.

In an example of troubleshooting and maintenance of (semantic)infrastructure and/or products/services, the system infers, receivesand/or determines abnormal/(NOT normal) and/or undesirable/(NOTdesirable) incidents and/or behaviors and as such the system goal is tofix, reduce and/or diffuse such incidents and/or behaviors. Thus, itinfers indicators and factorizations which optimize resonances andallocation of capabilities in rapport with the semantic identity, flux,destination, owner and/or user affected by incident and/or behavior. Itis to be understood that the system may further project fixes based onhigh entropy inferences in rapport the abnormal and/or undesirableinferences.

The system learns and/or is instructed with narratives regardingsemantic times of regular and/or occasional behaviors (e.g. JOHN SHOULDARRIVE BY NOON, THE PACKAGE SHOULD ARRIVE BEFORE THAT, THERE IS THESPRING MARATHON GOING ON, IT SHOULD BE QUIET TODAY, etc.). It is to beunderstood that such semantic times may be provided by users, semanticfluxes, leaders etc. Further, the system may provide guidelinesregarding the behaviors (e.g. (SEMANTIC WAVE) INFORM ME WHEN JOHNARRIVES OR THERE IS AN URGENT PROBLEM, EMAIL JANE WHEN THE MARATHONENDED, EMAIL ME AS SOMETHING UNUSUAL HAPPENS etc.). It is to beunderstood that the system may inform and/perform semantic augmentationbased on the received instructions, guidelines, semantic times,factorizations and/or further analysis.

The system may determine intrinsic behaviors based on drift, shiftand/or entropy of semantic times. In an example, the system maydetermine that there is one spring marathon (e.g. because the compositesemantic THE SPRING MARATHON is very specific, less diffusive and/orhaving less spread), THE SPRING MARATHON SHOULD OCCUR (80 EARLY) SPRINGand thus may infer that the MARATHON OCCURS SOMETIME BUT NOT ALWAYS, ONESPRING MARATHON 90% LIKELY, ONE EARLY SPRING MARATHON 70% LIKELY, ONESEASONAL MARATHON 50% LIKELY etc. and as such it determines that thefactorization routes/rules, shift, drift and/or entropy of MARATHON fromthe intrinsic behavior may be decayed for (EARLY) SPRING, NO MARATHONand/or further NO MARATHON, SUMMER, AUTUMN, WINTER semantic times and/orroutes. It is to be observed that a route NO MARATHON, SPRING comprisesat first the leadership cause of entropy or abnormality in regards tointrinsic (e.g. ONE MARATHON SPRING, expect one marathon in spring whichmay be invalidated by semantic time). Further, it determines thatoutside those semantic routes, rules and/or intervals the intrinsicbehavior is MARATHONS UNLIKELY, MARATHONS NOT LIKELY etc.

The system uses the leadership semantics associated with semantic times,endpoints and/or locations to define and/or create shift, drift and/orentropy for semantic identification (e.g. THE SPRING MARATHON, THE CHAIRBY THE FIREPLACE, THE DISPLAY POST IN THE CONFERENCE ROOM BY THE WINDOWetc.). It is to be observed that once the semantic route progresses theshift, drift and/or entropy changes—e.g. THE DISPLAY POST IN THECONFERENCE ROOM, BY THE WINDOW etc. As such, if the system has learned(been informed) and/or has a semantic route and/or trail comprising thesemantic identification and further identified only one display post inthe conference room then the more precise identification in the route BYTHE WINDOW provide further discrimination while keeping the drift, shiftand/or entropy low—since there is only one post in the conference room(e.g. potentially used as leadership and/or higher endpoint) then therisk that this is another post is low unless other inferences mayincrease the risk and/or entropy—e.g. door was not blocked/locked); ifthe system identifies more display posts in the conference room then BYTHE WINDOW provides lower shift, drift and/or entropy if there is adisplay post in the further discriminatory endpoint and/or location.Further, there may be higher shift, drift and/or entropy if there is no(display) post in the further discriminatory endpoint and even higher ifthere is no (display) post in the leadership discriminatory endpoint(e.g. CONFERENCE ROOM). In some examples, the system uses suchinferences to identify and/or authenticate artifacts which connect to(local) networks; in an example, a display is registered in a registryas DISPLAY IN CONFERENCE ROOM BY THE WINDOW and is further identified,renamed, updated and/or authenticated to DISPLAY IN CONFERENCE ROOM BYTHE PROJECTOR based on low shift, drift and/or entropy of compositeinferences; by high (entanglement) entropy, the display is notauthenticated if the shift, drift and/or entropy is high.

It is to be understood that when the identification confusion is higherthe system may further challenge for further localization and/ordiscrimination (e.g. WHICH DISPLAY (IN CONFERENCE ROOM)?—THE ONE BY THEWINDOW).

It is to be understood that the term “leading” as used in thisapplication may be associated with semantic leadership or not. Thus, theterm may be associated with a common/used/plain interpretation and/orsemantic leadership.

The system may associate deceptive and/or further associated synonymfactors/indicators to systems which publish resonant semantics and/orbudgets in affirmative semantic groups to achieve not publishednon-affirmative goals with the semantic group. When deception isinferred and/or factorized the system may decrease believability factorsand/or further factorize non-resonant, non-affirmative resonant and/orfoe factors.

Users and/or collaborators may be biased based on their model. In orderto counteract such biases, the system may challenge the user and/orcollaborator to explain why the biased statement, decision etc. In someexamples the biases occur due to selectivity and/or controlled semanticspread of information fed to the user/collaborator.

Affirmative and/or non-affirmative resonance factors may be associatedwith environments and/or semantic views based on at least one semanticprofile (of a user, post, semantic unit etc.).

The fear factors may be factorized based on unknown inferences innon-affirmative environments and/or semantic views. Analogously, thefear factors may decrease with inferences in affirmative environmentsand/or semantic views.

It is to be understood that the increase in the factors associated withparticular indicators and/or synonyms may trigger decrease in thefactors associated with the indicator antonyms.

The system may use (projected) risk factors, uncertainty and/or furtherstress factors wherein such factors increase with the confusion and/ornon-affirmative resonances within an interval and/or range between aminimum and a maximum budget and/or (semantic) time.

In order to reduce risk, uncertainty and/or further stress the systemmay pursue more immediate (e.g. less distant, less expensive, withincurrent budget etc.) goals, semantic routes, artifacts and/orinferences. In an example, the system projects that at least twosemantic routes would provide budget increases with the farther awayprojection providing a larger budget and/or reward; the system mayprefer the projection providing the lesser increase in the budget if therisk and/or uncertainty between the time of the first projection and thetime of the second projection are higher than a threshold and thusdecaying the affirmation and/or resonance of higher reward. Further, thesystem may bias the projection semantic time boundaries; within theprojection interval, the system may use risk and/or uncertainty torewards factors and/or thresholds; the system may prefer the projectionwith the lesser risk/uncertainty to reward ratio.

The system may associate rewards with affirmative resonance; further, itmay associate risks with non-affirmative resonance and/or non-resonance.

The system may be biased based on learned budgets and/or thresholdsassociated with artifacts and/or semantic groups thereof. As such, itmay not pursue a goal if the budgets and/or risk required to acquire afirst artifact associated with the goal inference and/or projection arehigher than a previously learned budgeting interval; stress anddissatisfaction factors may also increase during such inferences.Further, the system may pursue the goal if the stress and/ordissatisfaction factors (in relation with the first artifact) arereduced by inferring and/or being presented with alternative choicesand/or inferences which require even larger budgets and/or risks forartifacts associated with semantic groups comprising the first artifact.

The system may overestimate by positively and/or affirmatively index,factorize the current and/or “earlier” satisfaction, trust, leisureand/or affirmative factors and underestimate by negatively and/ornon-affirmatively index, factorize the same factors associated with a“later” achievement of a goal as the projected risk and/or uncertaintyfactors increase. Further, the system may underestimate earlierdissatisfaction, concern and/or stress factors while overestimating thesame factors associated with a later achievement of the goal. It is tobe observed that the (entangled) entropy may also determine and/or bebased on semantic time “earlier” vs “later”. As such the system may bebiased to factorize “earlier” affirmative factors vs “later” affirmativefactors and/or further decay “earlier” non-affirmative factors vs“later” non-affirmative factors.

The system may overestimate by inferring semantic times and/or semanticindexing based on capacity, demand and/or factor of consumption (e.g.demand vs capacity factor, STOCK/RECEIVED vs SOLD/EXPEDITED ratio/factoretc.). In an example, for a limited capacity, high demand and/or furtherhigh rate of consumption (e.g. of articles, budgets etc.) the system mayoverestimate the risk of loss, reward of gain, likeability and/or budgetbecause of inference of higher risk and/or uncertainty related with a“later” vs an “earlier” semantic time. Analogously, based on high(entanglement) entropy, for a larger supply, low demand and/or furtherlow rate of consumption of articles the system may underestimate therisk of loss, reward of gain, likeability and/or budgets because ofinference of lower risk and/or uncertainty related with a “later” vs an“earlier” semantic time. The system may adjust and/or bias the capacity,supply, demand and/or budgets/price to optimize logistics and/orbudgets. In some examples, those may be adjusted to projectoverestimation and thus increasing the turnover. In further examples,when the logistic infrastructure is in a critical/hazardous state and/orclose to the maximum capacity, it may be adjusted to projectunderestimation and thus decreasing the burden on the supply chain.

The system factorizes indicators as friend/foe at particular semantictimes. The system may index (projected) capacity (e.g.MANUFACTURED/RECEIVED/SUPPLYE/STOCK and/or composite/similar,(storage)space/locations) and/or projected demand (e.g.REQUEST/(BACK)ORDER/SOLD/EXPEDITED and/or composite/similar etc.). Whenthe demand overshoots the capacity it factorizes capacity as a friendand the demand as a foe and use further hostility and/oroffensive/defensive behavior analysis; analogously, potentially byH/ENT, when the capacity overshoots the demand it biases the capacity asa foe and the demand as a friend. Further, based on high (entanglement)entropy between capacity vs demand it may further undershoot one vsanother.

The capacity and demand may be related with semantic attributes,endpoints and/or links in the semantic network model.

The capacity and demand may be related with the availability andfeasibility of artifacts in the semantic model. In an example, thesystem infers that when the capacity over-weighs and/or overshoots thedemand, the availability of feasible zones, endpoints and/or links mayincrease. Analogously, by H/ENT, when the demand over-weighs and/orovershoots the capacity the availability of feasible, endpoints and/orlinks may decrease.

Capacity and/or demand projections may be used to determine the optimaldistribution and/or further contracting clauses associated withparticular artifacts, fluxes, endpoints and/or locations in particularcircumstances.

While capacity and demand in a supply chain and/or retail environmenthas been exemplified it is to be understood that such techniques may beapplied in any environments and chains based on capacity/supply anddemand/consumption (e.g. energy supply/grids, networking, computing,I/O, sensing, meshes, budgeting, trading, location/localization, assetportfolios, social networks, asset management, traffic, logistics,transportation, sports etc.). Further, it is to be understood thatcapacity and demand may be considered on a semantic group basis (e.g.FRIENDS OF JOHN, FOES OF DOES, OFFENSE, DEFENSE, RIGHT WING etc.).

The system may use overestimation and/or underestimation to inferfriend/foe. As such overestimation/underestimation on competing goalsand/or artifacts may correspondingly determineoverestimation/underestimation of foes and/or threats.

The system may identify threats and/or further foes by associating fearfactors with particular semantic identities.

The system may gate news, messages, emails, images, videos and othermultimedia artifacts based on believability, friend/foe and/oropinion/analysis factors.

The believability factors may be factorized based on an indexing factorassociated with the orientation and/or rate of achieving and/orfactorizing semantic resonance.

The system may identity friend/foe in order to gate content and/or flows(between/from/on posts, displays, websites, networks, traffic lanes,traffic lights/stops etc.).

The system may use friend/foe analysis for optimizing traffic flows(e.g. detect bottlenecks and/or mitigation).

The system may use counter bias factors of “later” vs “earlier” semantictimes to increase the semantic spread.

The system may use the “later” vs “earlier” inferences in order toassess and/or index speed, rate of orientation (increase/decrease,gradient) and/or distance semantics factors.

We mentioned that the system may use biases to overcome confirmationbias. In further examples, the system may strongly factorize artifactswhich are kept in cache, not decayed and/or not invalidated. As such,the system may be biased towards applying and/or being LIKELY to applythose routes whenever new inferences occur and thus bias the projectedinferences toward such artifacts. In such cases the system may apply abias to decay the factorization of such routes based on inferences whichincrease the semantic spread in the semantic flux network.

Overestimation and/or underestimation biases may be used duringuncertain/unknown (e.g. high confusion, low believability) inferences.

The system may determine confusion factors in collaborators based onsemantic flux inference, diffusion and/or direct challenges fromcollaborators.

The system may challenge collaborators to connect and/or challenge oneanother. This may happen when the system cannot reduce confusion incollaborator, when non-affirmative resonance is high and/or when thebudgets are tight. In further examples, when leader, the system maychallenge the collaborators to form a semantic group and/or furtherperform inferences and/or challenge on a composite basis; the system mayassign a particular semantic identity to such groups. In an example, thesystem may challenge John and Jane to take actions (e.g. entangle, entera relationship, connect, diffuse, allow etc.) and/or further formallyform a semantic group (e.g. DOES, DOE family, transport molecule/celland bind cell/protein etc.) and/or constraint bound by a contract andfurther comprising (contractual) collaboration clauses between/withinthe group and/or the system.

When the contractual clauses are not respected (e.g. overshoot and/orundershoot, are not within a resonant interval etc.), are violatedand/or there is high confusion, decoherence, less affirmative resonance,high dissatisfaction, less friendliness, less motivation and/or lessstimulation the semantic groups may expire/invalidate; it is to beunderstood that the expiration/invalidation of the group may determineincreases/decreases in positive/negative polarity and/or changes inpolarization. The trails of action associated with the formation of thecontractual group may be further decayed and/or updated to reflect thegroup's failing clauses; alternatively, and/or in addition, new semantictrails may be learned and/or recorded. Consequences of actions includingassociated artifacts may be pursued, factorized, learned and/orinvalidated with/for group expiration—e.g. (based on)disentanglement/decoherence, collapse, disconnect, block etc.

When presented with multiple routes in uncertain/unknown circumstancesthe system may be biased to overestimate the risk of the lower budgetroute while may underestimate the satisfaction/reward of the higherbudget route.

Further, the system may overestimate/underestimate the satisfaction withan option in a domain if an associated semantic identity is high/lowfactorized in another domain especially if the domains are affirmativeresonant.

The system uses earlier and/or later indicators and/or factors which maybe represented as (entangled) high entropy artifacts. In some examples,the earlier and/or later indicators are entangled in a composedindicator (e.g. urgency indicator and/or related).

The system may determine earlier and/or later indicators and/or factorsbased on semantic time management and/or time budgets/costs.

The earlier and/or later indicators may be used to counter-bias and/ormanage memory storage.

The system may overestimate artifacts which are associated with(earlier) cache/short-term semantic storage and/or underestimate theartifacts associated with long term (later) semantic storage.

The system may underestimate the future non-affirmative inferences andoverestimate the current affirmative inferences when the behaviors areintrinsic and/or with little shift, drift and/or (entangled) entropyfrom the intrinsic.

The system may use challenges and/or induce overestimation and/orunderestimation in friends, foes and/or network based on various learned(resonant) semantic profiles (of friend/foes).

The system infers anxiety factors based on increased confusion and/oroverestimation (of a threat and/or rate of change of threat factors) inrapport with a projected circumstance. Further, when the anxiety factoris elevated due to a blocking and/or foe artifact (e.g. route, endpoint,link etc.), the system may look to use alternate projections thatincrease resonance and/or diffusiveness.

The system may use a biased threshold of semantic route collapses toperform projections.

The system inferences and/or challenges may be related with achieving amaximum number of affirmative resonances and/or further friend biasedartifacts. The system goals and/or motivation (factors) may bebased/factorized on such inferences; in some examples, it can be used inrelation with defensive and/or offensive behaviors in markets and/orsemantic fields.

The system factorizes motivation in rapport with (pursuing) a transitionand/or a semantic artifact (e.g. route etc.) based on projections usingthe leadership/drive/orientation of the artifact which may decaydissatisfaction, concern and/or stress factors; the higher the rate ofdecay (or steeper shift orientation) of such factors in projections, thehigher the motivation factorization and/or indexing might be.

The system may infer that groups of players have goals for dominatingand/or maintaining relevance in a market even if they have less coherentcapabilities, solutions and/or strategies in rapport with the marketgoals. It is to be understood that the capabilities may be related withonly those allowable and/or possible for such players at particularsemantic times. In some examples, the capabilities and/or factorizationsmay be added, eliminated and/or adjusted based on parsing of capabilitydocumentation, patents and further semantic analysis etc. The system mayprovide a more affirmative bias towards more friendly, less hostileand/or more coherent competitors of such less-coherent groups.

In some examples, the system parses the content of this application toinfer the rules of semantic inference.

The system compares with past resonances and thus projects into thefuture. In some examples the system may overestimate and/orunderestimate the resonance based on learned biases and/or behaviors.Further, the system may overestimate and/or underestimate the shift,drift and/or entropy in rapport with semantic trails.

The non-affirmative overestimation (e.g. overestimating and/or biasing(based) on/of non-affirmative artifacts, factors and/or resonances) maybe based on defensive behaviors while the non-affirmativeunderestimation may be based on offensive behaviors. Analogously, basedon high (entanglement) entropy the system may infer affirmativeoverestimation for offensive behaviors and/or affirmativeunderestimation for defensive behaviors.

The system may perform semantic orientation based on usingoverestimation, underestimation and/or a composition of the two andthus, combining offensive and/or defensive behaviors.

The offensive and/or defensive behaviors are associated with artifacts,actions and/or learning which block foes inferences, actions and/orprojections. Analogously, potentially by high (entanglement) entropy,the offensive and/or defensive behaviors are associated with artifacts,actions and/or learning which allow friend inferences, actions and/orprojections. As explained previously, entities may be in a superpositionof friend/foe and thus the system may diffuse and/or collapse theoffensive and/or defensive behaviors based on superposition reductionand/or conditioning.

The system may pursue narratives which compares a choice (an option, aroute, a semantic artifact etc.) with the worst-case projections inorder to increase likeability and/or affirmative resonance with thechoice (and/or decrease the dissatisfaction and/or non-affirmativeresonance). Analogously, the system compares with the best-caseprojections in order to decrease the likeability and/or affirmativeresonance (and/or increase the dissatisfaction and/or non-affirmativeresonance).

Worst-case or best-case semantic artifacts (e.g. routes, trails,endpoints etc.) may be based on the lowest believable and/or borderlineresonant inferences which project high shift, drift, entropy in rapportwith an orientation.

Worst-case scenarios may be based, on the highest non-affirmativeresonant consequences; this worst-case orientation may be projected whenthe system overestimates in defensive mode and, by (entanglement)entropy, when underestimates in offensive mode. Alternatively, or inaddition, the worst-case scenarios may be based on the lowestaffirmative resonant consequences when the system underestimates indefensive mode and/or overestimate in offensive mode. By high(entanglement) entropy with the worst-case scenarios and itsorientations, best-case scenarios may be based on the highestaffirmative and/or lowest non-affirmative resonant consequences and/orfurther underestimation and/or overestimation in defensive and/oroffensive behaviors/orientations.

It is to be understood that the system uses high (entanglement) entropyto infer and/or analyze best case scenarios in comparison with theworst-case scenarios.

We presented system's capabilities for trading and/or bargaining. Insome examples, the semantic anchoring may be based on an (anchor)price/budget threshold used at the beginning of bargaining and/ortrading related inferences. The system may use overshoot/undershootchallenges and/or inferences to adjust the bargaining anchors.

During bargaining the system may use undershoot challenges and/orinferences in comparison with the current orientation. If the currentorientation has high drift, shift and/or entropy from a desiredorientation and/or overshoots in a semantic time then the system maycease to pursue bargaining on the particular flux; alternatively, it mayadjust the bargaining and/or trading anchors.

It is to be understood that the system may comprise intrinsic highlyfactorized/hard rules and/or routes that provide undershoot guidancefrom the current orientation when bargaining. However, the undershootanchors may change and/or the system may enter more stimulation phases(e.g. increased stimulation at semantic times) and thus, the system usesstimulation to bias budgets, offers and further inferences.

In bargain type inferences the system's goal is to achieve (individuallyand/or part of an affirmative resonant group) affirmative resonance witha bargaining partner and/or group; further, the resonance may be relatedwith achieving routes and/or goals (e.g. “good deal”, “develop repairskills” etc.) and/or sub-goals (e.g. “gain tuition budget”) with morelikeable and/or less stressful factors.

Although the parties in trading may orient on achieving different goals,the different goals collaborative inference should collapse into theresonant goal inference (e.g. “gain a good deal”, “develop repairskills”, “gain a good deal while developing car skills”, “develop repairskills for a good deal”, “get a good deal on car repair” etc.).

A trading and/or bargaining partner can be factorized as friend/foe.When the system factorizes the bargaining party more as a friend, thesystem may index down (decay) the resonance thresholds and/or index up(factorize) the resonance factors. Analogously, when the systemfactorizes the bargaining party more as a foe, the system may index upthe resonance thresholds and/or index down the resonance factors.

In bargain type interfaces the system may recommend activities and/orchallenges via semantic augmentation which may increase/decrease, indexand/or damp affirmative/non-affirmative resonance between partnersand/or increase/decrease the friend/foe factors.

We mentioned that the system may employ diversification strategies tooptimize stability of goal and/or further inference development.However, in some situations the diversification strategy may not befeasible and/or available and thus the system may infer “critical” typesemantics for particular artifacts, fluxes, streams and/orcollaborators. In some examples, logistic providers B and C provide tosystem A similar critical semantic identities and/or capabilities (e.g.““sanitizer”, “grade A””) which are used by the system A for criticaloperations (e.g. which bear high consequential hazardous semantics ifnot available and/or not performed); if one of the system B and Ccapabilities (e.g. B and/or its sanitizer grade A capability) is nolonger available the system may further increase the criticality factorand/or further leadership of the other system (e.g. C) and itscapability. In some cases B and C are within a semantic group, resonantand/or entangled to system A; while the resonance and/or entanglementmay not collapse when the capability of B is not available, factors ofthe resonance and/or entanglement may change; further, the resonancefactors may determine the entanglement resonance. However, the resonanceand/or entanglement may collapse if the capability is not availableand/or (incoherently) impacting the inference on the (composite) goalsof A and/or further stability of the goal (e.g. shift, drift, entropyfrom projections etc.).

We mentioned the use of forward and/or backward projections for semanticanalysis development. Such projections may proceed based on semantictrails and/or further semantic chains including semantic timemanagement; the system may project based on goals and/or budgets untilachieves particular coherency, resonance, entanglement/grouping,factorization (e.g. likeability/preference/satisfaction etc.) and/orfurther high entangled entropy factors (incoherency, non-affirmativeresonance, non-likeability, dissatisfaction etc.).

The system may express doubts and/or discrimination challenges. In someexamples the system expresses doubts and/or discrimination such as I DONOT THINK THIS IS A GOOD IDEA and/or further high entanglement entropyequivalents (e.g. with low drifts, shifts, low (entanglement) entropy)such as I DO THINK THIS IS A BAD IDEA. It is to be observed that theterm THINK is related with expressing (e.g. by the system, user etc.)affirmative and/or non-affirmative doubt and/or discrimination bias inrapport with a projected inference and/or outcome. As such, the systemmay use discrimination factors which may be factorized accordingly (e.g.a discrimination factor associated with a (DO) THINK (composition)and/or related semantic artifacts is affirmative resonantly factorized;by entangled entropy inference, a DO NOT THINK is non-affirmativeresonantly factorized. Since THINK related inferences may express moredoubt, potentially based on semantic profiles, than more assertive (e.g.THIS IS A BAD IDEA where DO THINK is implied) compositions the systemfactorizes and/or diffuses the discrimination toward the upper (e.g. forDO, THINK) and/or lower (e.g. for DO NOT, THINK) of an affirmativeresonant interval while allowing for larger damping and/or hysteresis(e.g. by indexing bias). In cases of more assertive constructs thesystem factorizes and/or diffuses the discrimination toward the upperlimit (e.g. for DO, THINK) and/or lower limit (e.g. for DO NOT, THINK)of an affirmative resonant interval while allowing for lower dampingand/or hysteresis. Analogously, for non-affirmative resonant constructsthe system uses high (entangled) entropy factorizations (e.g. factorizesand/or diffuses the discrimination toward the lower (e.g. for DO, THINK)and/or upper (e.g. for DO NOT, THINK)). It is to be observed that higherfactorization of a non-affirmative construct (e.g. BAD IDEA) maydetermine higher DO NOT factorized artifacts and/or rules.

The system may further use the semantic time management, collaborativeenvironment and/or semantic constructs in order to infer, optimizeand/or perform actions. In an example the system has a rule and/or routefor a construct such as BEFORE FALLING ASLEEP (ADJUST) THE MUSIC(DEVICES) TO A VOLUME THAT I LIKE OR TURN THEM OFF. It is to be observedthat the system projects that it goes to sleep and thus performs anaction based on a semantic route, rules and/or profiles. However, if theprojected inference and/or required budgets for performing the actionare high and the system doesn't have circumstantial coherentunderstanding it may want to challenge about the device (e.g. from anadditional flux) IS THE (MUSIC) DEVICE (STILL) ON? WHAT'S THE (CURRENT)VOLUME? etc. Further, the system may perform challenges and/or confusionreduction by other active semantic profiles which may be affected by theaction (e.g. of some other persons and/or groups affected by the actionand/or diffusion of the action). While in the example, we specifiedimplicitly (e.g. by VOLUME associated with a leadership factor/indicatorof the music device) and/or explicitly the semantic identity of theMUSIC DEVICE it is to be understood that other devices may be implicitlyand/or explicitly considered (e.g. FALLING ASLEEP may beinferred/related with biological signals from a biological sensor/deviceetc.).

The system may challenge collaborators for past, current and/orprojected inferences (e.g. WHAT WAS THE VOLUME BEFORE FALLING ASLEEP?).Further, it may express likeability in relation with such challenges andfurther update the semantic profiles with the semantic trails/routesand/or rules at the point of challenge.

If the system maintains unaltered and/or un-entropic intrinsicbehaviors, it may not need to perform challenges on those behaviors.

The system may be biased tooverestimate/underestimate/overshoot/undershoot factors and/or furthercapabilities, demand, consumption etc.

The system may use biasing and/or semantic analysis on both defensiveand/or offensive behaviors to counter biasoverestimate/underestimate/overshoot/undershoot inferences.

When the likeability and/or desirability is high the system mayoverestimate/overshoot the demand; further, when assessing usefulnessand/or acquiring a likeable artifact the system may overestimate therisk of loss and/or underestimate the risk of gain and thus, enteringoffensive behaviors and projecting goals/plans of gaining the desiredartifact. When the likeability and/or desirability is low the system mayunderestimate/undershoot the demand; further, when assessing usefulnessand/or acquiring the less desirable artifact the system mayunderestimate the risk of loss and/or overestimate the risk of gainthrough projections. It is to be understood that by high (entanglement)entropy the system may pursue reward of gain analysis instead of risk ofloss and/or further reward of loss instead of risk of gain.

If the projections are not feasible and/or do not match the truth thenthe system may factorize dissatisfaction, concern and stress factors inregard to projection plans.

In some examples, the system may bias the projections, goals,orientations and further factors, damping, hysteresis and thresholds toovershoot and/or undershoot wherein the overshoot may be based ondecaying non-affirmative overestimation and/or factorizing affirmativeoverestimation; analogously (e.g. by H/ENT), the undershoot may be basedon decaying non-affirmative underestimation and/or factorizingaffirmative underestimation.

In further examples, a likeability/desirability overshoot may beinferred/based on the decaying of non-affirmative and/or factorizationof affirmative resonances; analogously (e.g. by H/ENT), alikeability/desirability undershoot may be inferred/based on thedecaying of affirmative and/or factorization of non-affirmativeresonances.

Likeability/desirability overshoot, undershoot, overestimation, and/orunderestimation analysis may be used in inferring the demand, capacityand/or further advertising campaigns. Further, such techniques may beused to delimit (e.g. between overshoot and/or undershoot) the optimal,targeted and/or resonant zones, locations, hysteresis/dampingzones/limits/factor/ratio/orientation, spread and/or endpoints fororienting, rotating, focusing, stocking, transitioning, placement,inference, movement, marketing, conditioning, routing, operating points,intervals, semantic spread etc.

In order to counter-act biases the system may challenge friends abouttheir opinions and/or analysis on particular artifacts; further, inorder to increase the semantic spread, the system may challenge aboutfoes and/or theirs goals in regards to those particular artifacts.

The system identifies foes which, although may have similar goals, theyhave and/or are in a different semantic view which may compete (with thesystem or other semantic identity) for the same resources and/orresonances on tactical and/or strategical goals; thus, their goals areassigned foe signals and/or a high (entanglement) entropy with thesystem's (or another semantic identity) goals (e.g. I LIKE JANE, JOHNWANTS TO DATE JANE, JOHN IS PURSUING RESONANCE WITH JANE, JANE (DINNER)TIME AND/OR (DINNER) FAVORS ARE GAINED BY JOHN, JANE UNLIKELY AND/ORUNABLE TO DATE ME, JOHN IS A 51% FOE).

It is to be observed that the competing goals may be based on(projected) availability of particular semantic identities, capabilitiesand/or artifacts at particular semantic times and further based onsemantic profiles (e.g. “Jane very likely dates, can be entangled and/orresonate with one person”, “JANE dates, is 80% entangled and/or 80%resonates with BILL”; “JANE is busy for dinner” etc.).

While the system identifies foes, it may overestimate and/orunderestimate the loss or gain and enter offensive and/or defensivebehaviors. Analogously, the system identifies friends when they havesimilar competing tactical goals (but the system may underestimateand/or overestimate the loss or gain for strategic goals) and/or they donot compete for the same goals, resources and/or resonances. It is to beunderstood that the overestimation and/or underestimation may depend onsemantic time (e.g. the system may overestimate/underestimate thegain/loss during a competition and/or underestimate/overestimate afterthe competition).

It is to be observed that double high (entanglement) shift, drift and/orentropy (e.g. risk vs reward, loss vs gain) and/or further compositions(e.g. risk of loss, reward of gain etc.) have/determine low entanglemententropy and/or synonyms thus, allowing the system to perform furtherlearning, groupings and/or factorizations.

The system identifies competition and/or competing semantic identitiesby identifying resonances of semantic identities in rapport with thesame and/or similar goals. It is to be understood that the similarity ofgoals may be based on low semantic shift, drift, entropy and/or highresonance.

The system may perform learning based on an approval and/or relatedsynonym factor. The approval factor may be inferred based on affirmativeresonances in rapport with leadership goals.

The system may be biased to project semantic routes which are similarwith previous inferences unless those are expired and/or invalidated.

The system may receive trajectories on rendered graphs and/or chartsfrom the user expressing desires regarding behaviors associated withvarious parameters and/or semantics. In some examples, the systemrenders a timeline of (composite) oxygenation (e.g. “oxygenation”,“oxygenation in vitro”, “oxygenation, in vitro” etc.) of a biologicalsensor/actuator and the system specifies the desired trajectory of theoxygenation which relates to specific commands, currents and/or voltagescontrolling to the sensor/actuator. While the timeline may be basedsolely on absolute time, it also may also comprise semantic timemanagement; in case that the timeline comprises semantic timemanagement, such semantic times on the time axis may be specified by theuser (e.g. from selecting from a list, label, control, speech etc.)and/or be presented to the user by the system. It is to be understoodthat there may be multiple semantic timelines, graphs and/or chartspresented to the user for the same semantic; further, the timeline maycomprise semantics which have high entropy/drift (e.g. “oxygenation “inhabitat environment””) with the composite semantic (e.g. “oxygenation“in vitro””) and as such the system may learn semantic artifactsassociated with leadership semantics (e.g. learns semantic routes and/orrules for oxygenation factorization, indexing, hysteresis and/or dampingin particular circumstances and entropy factors).

The user may specify trajectories representing voltages and/or currentsof actuation, command and/or sensing. The system may infer hysteresisassociated with various semantic profiles based on parts of thespecified trajectories and determine whether the subsequent parts of thetrajectory are encompassed within the semantic drift, shift and/orentropy associated with the hysteresis and/or damping. As specified inthe previous example, the user may specifies the oxygenation, howeversince the actuation of oxygen tank releasing and/or evacuation actuatorshave a certain capability range (e.g. flow rate) the system may notprovide and/or diffuse sufficient oxygen in order to achieve the usertrajectory. Thus, the system may infer regions encompassing thetrajectory based on the device hysteresis and further infers thesemantic shifts, drifts and/or entropy. If the trajectory is notencompassed in the operating regions then the system may adjust thesystem indexing, hysteresis and/or damping to encompass portions of thetrajectory based on various criteria (e.g. semantic time, factorization,semantic indexing, maximum containment, minimum containment etc.).

Analogously, the system analyzes attributes in charts and/or UIcomponents. In some examples, the system comprises a chart depicting animpact (e.g. “driving alertness” “in” “high pollen locations”) onsemantic groups of “allergic driver” wherein further, the system maydrag and drop an artifact (associated with) of diabetes and/or furtherdiabetes treatment on the sampling group attributes and thus the systemmay infer, challenge and/or or render values for the compositesemantics.

We mentioned the use of semantic augmentation including composing,rendering and/or routing augmentation artifacts and/or modalities. Insome examples, the system composes messages, emails, documents,multimedia and/or renderings which incorporate summaries and/or subjectslines comprising high level leadership actions required from thedestination (and/or semantic groups thereof) to achieve leadership goalsof the sender in rapport with the destination. In some examples, thesystem requires a signature from JOHN in regard to the (resonant) goalof COMPLETE SALE OF THE FAVOURITE DE LOREAN and thus it composes a salesdocument and sends it via messages/emails comprising messages and/orsubject of PLEASE SIGN, DE LOREAN SALES CONTRACT.

In further examples, the system uses projections of semantic resonancesin rapport with the destination entities and/or semantic groups thereofto compose artifacts such as messages, narratives, multimedia, videosand/or other renderings. It is to be understood that the system may usesuch techniques for content, formatting, rendering, presenting, gating,access control etc.

The system may include timelines for a destination and/or semanticidentity requiring attendance in a semantic time (e.g. PLEASE SIGN THEINSURANCE PAPER BEFORE LEAVING ON HOLIDAY, PLEASE RESPOND ASAP, PLEASEFIX THIS ISSUE FIRST, PLEASE FIX THIS ISSUE BEFORE JOHN IS ARRIVINGetc.). In some examples, such time sensitive requests may be linkedand/or associated with subject lines, summaries, paragraphs, taggedartifacts, text content, renderings, UI (aka user interface) tags, uicontrols etc. Further, the system may factorize the resonance in rapportwith the (resonant) goal and the destination when the semantic timegoals don't expire (e.g. because “fixed issue before John arriving”);analogously, the system may decay the resonance if the semantic timegoals expire.

It is to be understood that the system may couple any device to thesystem, semantic flux network and/or semantic units by wired and/orwireless protocols. In further examples, the system buys and/or acquires(e.g. within the semantic flux network) an article, item, device, sensorand/or further semantic unit for which an identification (e.g. id, code,TPM, password, MAC address etc.) and/or key is made available for theacquirer and automatically added to a wallet which can be further usedfor authentication.

Various devices may be provisioned with the keys, wallet and/or parts ofthe wallet and thus they can be identified as belonging to the samenetwork, user, location, endpoint and/or further groups. Further opticalidentification and/or encoding techniques may be used (e.g. semanticwave, optical/QR/bar codes etc.). Even further, the system may askand/or use multifactor identification when pairing the device in thenetwork. While adding and/or pairing the device the system may transfer,encode and/or encrypt semantic rules to be used while pairing for thefirst (and/or) subsequent times. Further, semantic rules and/or furtherexplanation of the authentication, rules and/or signals may be used toconnect the device into the network.

The collaborative systems may assume the ownership of an activity, task,action and/or further circumstances. In some examples, the ownership maybe based on leadership inferences and/or challenges; further, resonantinferences may determine the assumption of ownership.

In cases where the system wants to delegate the ownership of anactivity, task, action and/or circumstance then it may challengecollaborators in regards with such actions while allowing for variousdegrees of resonance, confusion, concern and/or likeability in thecollaborator. In some examples, the system challenges the collaboratorswith a need while providing an eventual brief explanation of the needand/or its dependencies which may trigger resonant inferences comprisingthe identification of a leadership skill in the collaborator.

We exemplified the use of OPINION type inferences in variouscircumstances. Alternatively, the system challenges and/or is challengedfor ADVICE instead of OPINION wherein the semantic ADVICE is seen as amore inclusive and/or resonant than OPINION which doesn't require and/ordetermine resonance; instead, OPINION can be seen a critic patterncomprising a critic factor.

In some examples, the system may infer that particular data isassociated with advice, opinion and/or analysis and/or further associatecorresponding factors (e.g. for advice/opinion/analysis (of semanticidentity)); further, the system may infer and/or receive facts and/ortruths in the semantic field and factorize those accordingly (e.g. FACT90% etc.). In some examples, the truths in the semantic field are basedon quantifying high entanglement entropy groups (e.g. 100 of RECEIVEDAND EXPEDITED or (RECEIVED, EXPEDITED) mean 100 PROCESSED ORDERS etc.).

The system may rate opinion, advice and/or analysis based on semanticentropy, drift, shift, orientation and/or further analysis incomparisons with facts and/or truth semantic artifacts and/or semanticfields. Further, the system may gate such opinions, advices and/oranalysis if the rating is low (e.g. low rating means they are toobiased, false etc.). Further, the system infers and/or learns biasesbased on such inferences, semantic trails and/or further semanticanalysis.

The system may label and/or augment the renderings with inferences aboutwhether it is opinion and/or analysis. It is to be understood that theopinion may comprises analysis (e.g. of self and/or other collaborators)and/or vice-versa; similarly, analysis may comprises other analysis andopinions which further may comprise other opinions (e.g. in hierarchicalmanner). In an example, on semantic cloud media postings the system maylabel, mark and/or overlay the posted artifacts and/or groups thereofwith associated opinion analysis artifacts; in an example, on a semanticcloud the postings and/or flux data are published and is marked withOPINION (OF JOHN) (OF JOHN'S FRIENDS), ANALYSIS (BASED ON OPINION OFS2P2), ANALYSIS OF HEALTH OF S2P2 BASED ON JANE'S OPINION, OPINION OFJANE REGARDING S2P2 BASED ON S2P2's (FLUENCY) ANALYSIS OF THE AUTOMOTIVEMARKET etc.

The system may consider as truth in the semantic field the explanationsand/or further inferences generated by the (original) source of signalsand/or data. It is to be understood that the semantic flux and/or streaminformation may comprise semantic trails of semantic identities andtheir further profile artifacts which interpreted the data (e.g. basedon their own profile, model, opinion and/or analysis). Further, anysystem may decide based on such semantic traces and/or trails whether itcan trust the data or not; in some examples, such semantic traces and/ortrails may be comprised in semantic waves.

The system may rate and/or allow ratings of such analysis and/or opinionand/or further gate it based on semantic model. In some examples, theanalysis and/or opinion is rated and/or gated based on a highshift/drift and/or entropy from facts in the semantic field.

The system may gate the semantic artifacts which are based on opinionand/or analysis factors and/or ratings.

The system may infer and/or express (e.g. via augmentation, challengesetc.) critical opinions and/or analysis of inputs, artifacts and/orcollaborators. In some examples, the system analyzes and/or generatescritic essays against DOE'S baseball game performance. If the essaysentails critics comprising non-affirmative resonance of DOE'S behaviorwhich is not related (e.g. is non-resonant, has high shift, drift and/orentropy) to the essay's theme and/orientation (e.g. baseball gameperformance) then the system determines and/or infers a hostility factor(of the critic/essay) towards DOE'S and thus may damp, smoothen and/orgate such artifacts. The system may infer and/or suggest various DOE'Sleadership artifacts in various circumstances and thus, the indicatorsand/or factors toward DOE'S may highly diffuse to such leadershipartifacts. Further, the system may use high entropy comparative semanticanalysis of UNDOE'S baseball skills, games and/or performance.

We explained the use of semantic trails to keep track of timelines ofsemantic inference. In order to generate past semantic inferences and/orsemantic time the system uses semantic trails.

The system may use invalidation, confusion factors and/or challenges todetermine the tenses of opinions and/or constructs. In some examples thesystem infers that I LIKE JOHN, but it infers that John (artifact) isless resonant because it doesn't play baseball anymore and thus, expiresand/or decays likeability (opinion) factors. Further, when the system ischallenged with DO YOU LIKE JOHN? the system may use semantic trails toinfer the invalidation of likeability (e.g. I DONT LIKE HIM ANYMORE)and/or a less factorized term and/or composition for LIKE (e.g. IT'S OK,I LIKED HIM MORE BEFORE HE STOPPED PLAYING BASEBALL, I LIKED HIM MOREWHEN HE PLAYED BASEBALL). Analogously with likeability factors thesystem may infer high (entangled) entropy factors such dissatisfactionand/or non-likeability.

Hostility of a semantic identity may be factorized when such semanticidentity exhibit hostility towards friends and/or highly affirmativesemantic groups. In addition, the hostility is decayed when the semanticidentity exhibits offensive affirmative and/or defensive behaviorstowards friends and/or highly affirmative semantic groups.

The system may infer entangled and/or causal hostility comprisingsemantic identities.

In some examples, the system looks to decrease hostility factors byfactorizing, indexing and/or damping semantic spreads and/or resonanceintervals in order to achieve borderline affirmative semantic resonance(e.g. equal or barely higher than the lower affirmative limit).

The system may associate high increase in factorization withrecording/recordings, multimedia artifacts, renderings and/or furthersemantic artifacts. In some examples when the system detects increasedhostility it may start saving video/audio snippets, frames, imagesand/or further semantic artifacts; in addition, the system may furtherinfer and/or build internal rendering representations of such artifactsand/or scenes.

When there is entanglement between two semantic identities which havehigh hostility factors then the entangled and/or the observer (analyst)systems can infer regret indicators and/or factors towards actions whichdetermined causal inferences and further entanglement and/or hostilityfactorizations (increases).

When performing semantic inference on two and/or more semanticidentities the system may consider and/or substitute any semanticidentity with self during a semantic time and/or in the past,current/present and/or future. Further, the system may consider semantictrails, routes and/or further projections to infer hostility, regret,affirmative and/or non-affirmative factors towards his actions whileperforming analysis on the past, current/present and/or futureartifacts.

The system may express regrets about actions that it takes as opposed tonot taking action. In other examples, the system expresses regrets fornot taking action. Further, the system may express regrets for being toooffensive and/or too defensive.

The system performs access control, actions and/or gating based onfriend/foe and/or further hostility factors (e.g. allow friends and/orless hostile semantic identities, deny and/or pursue foes and/or morehostile, diffuse hostility etc.).

The truth in the semantic field may be a general accepted truth and/ortruths as accepted (ALLOWED), not-accepted (BLOCKED) and/or diffused bysemantic groups.

The truth in the semantic field may be based on fact semantic artifacts,general accepted ontologies and/or further quantities.

The system may challenge and/or decays the truth factors when it infersoverestimation and/or underestimation biases.

The system allows/disallows maneuvers within particular areas based onsemantic analysis, access control, semantic gating and/or semanticdiffusion.

We mentioned the expansion of semantic trails and/or routes in semanticviews. While such artifacts may comprise elements which are not relevantto the current inferences, goals and/or orientations in the semanticview, such elements may be invalidated by the system (e.g. viaexpiration time, semantic time etc.). It is to be understood that,during and/or after expansion, the system may preserve in the semanticview the semantic identifiers of trails, routes and their elements, andthus, the expansion of the semantic trail/route doesn't invalidate theirsemantic identifiers unless the semantic invalidation and/or semantictime demands it.

The system may be challenged, perform and/or challenge for particularsemantic identities of semantic views and/or further renderings. In anexample, the system may be challenged, challenges and/or accesses a“teach” view which perform semantic augmentation on teaching and/orcapabilities of the semantic flux/stream network.

The system may receive and/or challenge the semantic network about thesemantic identities and/or their capabilities which are at an endpoint,area and/or trajectory comprising current and/or projected locationand/or kinematics of the system. In addition, the system may receivecertificates and/or other authentication information related to suchsemantic identities.

The system may use semantic diffusion and/or further semantic analysisto determine the endpoints, areas and/or kinematics of projectedlocations.

The system may select, activate/deactivate, enable/disable semanticviews, windows, renderings, images, frames, videos and/or players basedon challenges, resonance and/or goals. In an example, the system ischallenged to select, activate and/or enable semantic artifacts whichare resonant with teaching, learning, teacher and/or student artifacts.

The system may deem as truth in the semantic field all the facts and/orsemantics that are inferred based on actual numbers and/or general(profile) rules inference.

We specified that the system assigns leadership based on detected depthanalysis. It is to be understood that the depth may be analyzed and/orprojected from at least one endpoint, view, anchor and/or viewpoint. Insome examples, the system analyses depth semantics from such multipleartifacts in the same time; such viewpoints may be based on target goalsemantic spread. The semantic system may use kinematics and/or semantictime of semantic shapes and/or groups detections to infer depth and/ordistance semantics.

As a (coherent) shape moves coherently on top of another (coherent)shape and the system infer depth and/or distance based on a (projected)dynamicity factor, projections and/or further “earlier” vs “later”inferencing and/or indexing (e.g. “earlier” projections are moredynamic).

The system may use challenges to the semantic network about semanticidentities and their intentions. The system may use semantic analysis toselect, enable and/or show various semantic artifacts, user controlinterfaces and/or windows. Analogously, potentially based on high(entanglement) entropy the system may deselect, disable and/or hidevarious semantic artifacts, user control interfaces and/or windows.

In some examples, the system explains the assessment of inferences basedon causality and/or semantic trails.

The system may determine a fluency indicator and/or factor, wherein thefluency factor is affirmatively factorized when there is little or noshift, drift and/or entropy for the orientation of factorization of a(inferred) leadership semantic artifact and/or group thereof associatedwith inferences and/or challenges in rapport with a semantic identity;it is to be understood that the shift, drift and/or entropy may be basedon semantic profiles. In an example, the system listens, views and/oringests a recording of John; the system infers that a leadershipsemantic attribute associated with John in the recording is AUTOMOBILECHASSIS and thus because during the recording the orientation offactorization of AUTOMOBILE CHASSIS is coherent, affirmative resonantand/or induces low confusion within a semantic interval then the systeminfers a high factor of fluency for John in rapport with AUTOMOBILECHASSIS semantic artifact; analogously, by entanglement entropy thefluency may be low if the orientation is incoherent, non-resonant,non-affirmative resonant and/or induces high confusion. Further, thesystem may project that John may be also be fluent in AUTOMOBILES,ENSSEMBLIES etc. based on semantic hierarchy, semantic routes, groupsand further shift, drift and/or entropy in rapport with an observingsemantic identity; the fluency is higher factorized when the semanticdrift, shift and/or entropy is larger in the challenge and/or resonantinterval (e.g. because Jane is not an expert in automobiles she mayfactorize John's fluency in auto industry; because John is more fluentin automobiles it may be promoted as a leader etc.).

In similar ways the system may determine fluency in languages, fluencyof traffic, fluency in teaching, fluency in marketing, positioning,plans, projects etc.

The semantic indexing and further grouping may be used to determine atrue orientation and/or resonance with a published indicator. In someexamples, a security is traded in such a way by a semantic identityand/or (affirmative resonant) group to influence its orientation. Insuch conditions the system may infer the intentions of the influencerbased on a desired trajectory and further shift, drift, entropy and/orindexing from the influencer's trajectory. If the trajectories aresimilar, then the system may use and/or infer resonant factors for theindicator in rapport with the semantic identity and furthercircumstances.

The semantic shaping may be associated with shape patterns in graphs(e.g. of securities, stocks, indices, signals etc.).

The system may use semantic inference towards the goals includingdamping, hysteresis, indexing, factorization, diffusion, resonance andfurther semantic analysis.

We explained the use of various gratings and/or meshes for sensing,communication and/or semantic processing. These gratings and/or meshesare coupled with semantic analysis in order to take advantage of theirquantum properties (e.g. spin orientation, entanglement, energy leveland/or quanta etc.), polarities, polarization fields, resonances,damping, interactions and/or semantic groupings thereof. As known in artthere are many approaches of taking advantage of the quantum propertiesincluding superconductors, ion traps, topological, optical, nuclearmagnetic resonance etc. As mentioned in this application the semanticinference, analysis, semantic flux/stream and/or semantic wave mayfunction on such architectures based on semantic entanglement.

The semantic resonance may be implemented and/or inferred based onvarious types of techniques generating resonant responses and inductionsuch as electromagnetic, acoustic, electric, quantum, nuclear (e.g.NMR), quantum/electron spin resonance (e.g. ESR etc.) etc. In anexample, the magnetic field in an inductor generates an electric currentthat charges a capacitor, and then the discharging capacitor provides anelectric current that builds the magnetic field in the inductor whichfurther determines the repetition of the cycle and the self-sustainingoscillation. The system may use semantic biases, damping, hysteresisand/or indexing to adjust components' and/or circuits biases, dampingand/or hysteresis and thus adjusting the self-sustaining oscillationand/or further associated semantic resonance. It is to be understoodthat the capacitor charge polarity and/or further current conditioningin inductors may be associated with semantic factor polarity.

Further techniques such as sympathetic resonance may be used. In someexamples, the sympathetic resonance induces and/or diffuses resonancebetween various semantic identities, semantic groups and/or hierarchiesthereof. Further, particular sub-groups and/or hierarchies may beresonant to only particular harmonics at a given resonant vibration,spin, damping, polarization and/or frequency.

The semantic collapse may occur with particular threshold energy and/orfrequencies. In some examples, the threshold and/or interval is based onthe resonant energy and/or frequency and further it is associated withsemantic resonance. It is to be understood that a resonant energy budgetmay determine resonance with the resonant frequency; further, theresonant frequency may determine and/or factorize the resonant energybudget while being damped with particular damping coefficients (e.g. andthus, decaying resonance).

The system may represents entangled semantic artifacts based ontechniques such as entangled photons, ions (trapped, diffused), spins,polarities, polarizations and further use electromagnetic control,sensing, resonance and/or other techniques based on semantic analysis.

The system may build plans, artifacts, documents, signals, waves,renderings, multimedia and/or streaming file by example and/or byguidelines. In some examples, the example provided may be a semanticidentity and associated causality. In further examples, the system maybe instructed to CREATE A PRESENTATION ABOUT JOHN'S SUCCESSES and assuch the system identifies causality links associated with successfulachievements of JOHN'S in rapport with particular semantic groups and/orprofiles. Additionally, or in further examples, the system is instructedto build a document and/or movie LIKE this other video, image and/ordocument. Further guidelines may be provided and thus the system buildssuch artifacts based on those guidelines, semantic profiles (e.g. of auser, semantic entity and/or groups thereof) and/or interpretation ofexamples. In further examples, the system mentions its preference and/orguidelines for artifacts (e.g. I LIKE THIS, I LIKE THIS MOVIE, I LIKETHIS MOVIE STORY, I LIKE THIS MOVIE IMAGE, BUILD ME SOMETHING SIMILARetc.) and thus the system uses provided and/or inferred leadership,likeability and/or resonance artifacts to generate the requiredartifacts.

The system may be instructed and/or instruct via challenges about whatit needs to execute. In some examples, semantic identities comprisingsemantic routes of semantic profiles are used. Further, the instructionsand/or challenges may comprise time management routes and/or sub-routes.

The system uses semantic analysis including diffusion, gating and/orrouting to activate semantic augmentation modalities. In some examples,the system comprises semantic routes for performing augmentation e.g.SHOW ON MY DISPLAY—intrinsic behavior, BIP TWICE WHEN SOMEBODY ISPRESENT, RAISE HAND WHEN CONFUSED, NOTIFY ME IF BILL IS HOSTILE etc.

The system may select leadership based on the projected risk and/ornegative consequences (e.g. non-affirmative, high undesirabilityfactors, high risk etc.). Thus if those factors are highly factorizedthen the system may follow more predictable leadership (e.g. followssemantic trails more closely etc.).

We mentioned the use of brokers and/or arbitrators, and they are furtheraddressed with reference to FIG. 25 . The brokers and/or arbitrators mayprovide information to insurers and/or act as insurers. The insurer maybe coupled to multiple brokerage services and use semantic factorizationfor multiple indicators which can be provided by such brokerages. Thesystem may generate new policies (e.g. by semantic rendering and/oraugmentation) and work as an arbitrator in disputes by having access tothe evidence through semantic augmentation, trails and/or furtherrecorded snippets and/or artifacts.

In further examples, an insurer may specify that particular behaviorsand/or clauses are covered while others are not. As such, the system mayinform the user when such behaviors are not met. In similar ways, theuser may describe to the system the (projected) circumstances,situations and/or behaviors and thus the system may behave and/orperform semantic augmentation based on such circumstances. In someexamples, the system may inform and/or challenge the system and/or usersof future happenings and/or whether to store and/or inform the brokerand/or insurance company about it. Alternatively, or in addition, thesystem may store, inform and/or perform semantic augmentation based onthe factorization associated with the inferences and/or further drift,shifts and/or entropy in regard to the knowledge at hand.

The system may calculate insurance premiums based on optimization ofbudgets, semantic factorization of various indicators includingindicators in rapport with competitors and their premiums.

The system may generate policies items and/or premiums based on semanticanalysis on competitors; further, the system may look to createaffirmative resonance with a customer/user while inducingnon-affirmative resonance in rapport with a competitor.

With further reference to FIG. 25 , an insurance provider isillustrated. The insurance provider may be a broker, an agent, oranother insurance entity. Preferably, the insurance provider operates aserver computer having a memory and processor with stored programminginstructions operating as an analysis engine to perform the tasks suchas adjusting or setting a premium, requiring particular insuranceclauses, or assessing fault as further described below. The insuranceprovider is in communication with one or more semantic robotic devices(including semantic robotic device 1 through semantic robotic device n),in which the semantic robotic devices may be in accordance with thedescription provided in this disclosure, having a memory, processor,programming instructions, and various sensors such as cameras. In oneversion, the semantic robotic devices are configured to analyze anincident (such as by capturing images or the like) and to generate areport including an opinion of fault. The report and opinion arecommunicated to the insurance provider, including its server computer,which uses such report to perform tasks including adjusting a premium,requiring one or more particular insurance clauses, and making coveragedecisions.

In further examples, the system insures semantic identities the risk ofloss, risk of gain, reward of loss and/or reward of gain of particularsemantic identities and/or artifacts.

The system may insure, by reverse H/ENT, artifacts using the similarand/or the same clauses, policies and/or premiums. In further examples,the system may decay the risk, hazard, non-affirmative factors and/orpremiums when there is low orientation, shift, drift, entropy fromcontract clauses and/or recommended behaviors; analogously, by H/ENT,the system may factorize the risk, hazard, non-affirmative factorsand/or premiums when there is high orientation, shift, drift, entropyfrom contract clauses and/or recommended behaviors. In further examples,the system challenges the insurer fluxes with a budget and/or furtherclauses.

The system, such as via the semantic robotic devices, may expressopinions/analysis on the reason and/or who's at fault when incidentsoccur; it is to be understood that the incidents are inferred and/orfurther interpreted based on semantic analysis and/or further guidelines(e.g. INFORM (ME) (AND) (LEADER) (S2P3) WHEN S2'S POSTS IN AREA 55 HAVEINCIDENTS, RECORD AND GIVE ME AN OPINION (OF LEADER) (OF S2P2) WHEN(S2P2) POST FLIPS, ASK FOR OPINION OF (SECURITY) POST S2P4 ON WHY S2P2FLIPPED, WAS S2P2 OR S2P4 HOSTILE? etc.). Thus, the system informsand/or records the information based on semantic analysis, rules and/orfurther based on semantic trails and associated snippets/multimediaartifacts at semantic times when such semantics are inferred.

The system may challenge collaborators to express their opinion and/oranalysis on the incidents.

The system may index the insurance premiums based on the semanticfactors inferred from the opinion/analysis of the incidents.

The system may transfer its opinion and/or analysis on the incidents tothe semantic network, at least one insurance provider and/or broker. Itis to be understood that the at least one insurance provider and/orbroker may be another semantic system.

The system may invalidate recorded data while the storage of such dataon particular devices and/or memories is considered un-important (e.g.(S2P3) KEEP ALL INCIDENTS OF S2P2 UNTIL YOU HEAR BACK, REMOVE ALL THEARTIFACTS/INCIDENTS OF S2P2 BEFORE IT FLIPPED BUT KEEP THE ONES WHERE ITTALKED CARS WITH JOHN, REMOVE ALL THE INCIDENTS OF S2P2 BECAUSE S2P3 HASTHEM etc.). It is to be observed that the incident artifacts can bestored in any other post and/or module (e.g. S2P3, S2P2, leader etc.);further, when the system doesn't have resources to store such artifactsit may challenge the system, user and/or leader about the previouslyrule, route and/or guideline which conflicts and/or has high entropywith the current state of the system (e.g. KEEP ALL INCIDENTS vs (MY)(S2P3) MEMORY ALMOST (99%) FULL).

It is to be observed that the S2P3 system may challenge back on theprovided guidelines, routes and/or rules; alternatively, or in addition,the system may take appropriate actions without challenging back and/orreceiving a response. This may happen for example when the systemassesses via semantic analysis that this is allowed and/or based onfurther goals and factorization inferences.

The system may augment, add, compose, maintain, invalidate, clear andallocate resources based on semantic projections, analysis and/orfurther on semantic group basis. In an example, the system determines asemantic group for informing and/or storing artifacts about incidentsbased on risk, risk of loss and/or further groupings.

The storage of artifacts at various posts and/or endpoints may be basedon semantic projections, budgeting and/or further analysis. In someexamples, the system stores incident multimedia artifacts at S2P2 andS2P3 maybe because the system projects that the S2P2 and/or S2P3missions and/or endpoints comprise routes which lowers the risk of loss(e.g. by being distributed in particular locations, circumstances and/orenvironments) etc. Further, the incident artifact storage posts may haveroutes which allow budget optimization, affirmative resonances and soforth.

While monitoring an area by a sensor, module, post and/or securityentity the system may factorize the security entity as friend and/orfoe. In some examples, the system factorizes the security entity as afriend because its goals resonate with the system's goals for the area.In other examples, the system may factorize the security entity as foebecause its goals have high entropy and/or are non-affirmative resonant.While the system may factorize the security entity as a foe, thesemantic flux from the entity may still be affirmatively factorizedand/or have low risk of distortion due to the fact that the securityentity may not be aware of the system's foe inference/designation, notconsider the system as a foe and/or being bound by contractualobligations.

The system may infer the income and/or further budget associated with aparticular semantic artifact based on incoming and/or outgoinginferences and/or challenges associated with the particular semantic(artifact); it is to be understood that the inferences and/or challengesmay comprise semantic analysis and/or chains associated with theparticular and/or similar semantic.

The system may couple budgets and/or funds to financial/insurance fluxesand/or entities. In some examples, a user is connected to multiplefinancial providers, insurance, banks, securities brokers and/or othersimilar entities and thus selects the optimal budgets, rates, premiumsand/or funds to be applied to particular transactions based on semanticanalysis.

The system may negotiate contracts, clauses and/or conditions. Theclauses may be based on semantic time.

In some examples, the system negotiates (interest/insurance) rates,prices, budgets, semantic intervals and/or semantic factors to be within(affirmative) resonant intervals.

The system may use overshoot and/or undershoot type of inferences foroperating intervals on liquidities and/or further liquid budgets.

The system may infer offensive and/or defensive behaviors of the marketmakers and/or leader players in various verticals. Further, the systemmay infer the liquidity at/for particular endpoints and/or semantictimes.

The system may ingest financial assets and liabilities and infer theoperating (interest) rates intervals; further, the system may performrouting within semantic flux network for buy/sell assets, challenges tocurrent/potential customers, marketing campaigns and/or furtherchallenges to optimize liquidity.

The system may express budget, demand and/or capacity goals. In someexamples the system is instructed and/or infers (e.g. from modalities)to optimize consumption so it can have less stress within a semantictime. In further examples, the system saves and/or spends budgets atsemantic times based on inference on behaviors and/or further factors.

The system may determine goals for income and/or profits based onsemantic analysis and further semantic publishing. The income and/orprofit is positively factorized when the (traded) budgets gains ofincoming challenges on published semantics is higher than the budgetsloss on the inferences and/or (traded) outgoing challenges. In someexamples the factorization comprises extracting the budget losses frombudget gains; however, in other examples the factorization is based onfurther formula inference and it may be semantic time dependent.

When profits and/or budgets are high at an artifact, the artifact mayseek to further expand, publish additional capabilities and/or dividewithin and/or outside the original hierarchical (leader) endpoint.However, the divisional entity may still be biased and/or highlyconnected to the original hierarchical (leader) endpoint and generateincome and/or profits for that endpoint.

The system may be biased to syndicate such asinferring/having/determining/adhering to the same hierarchical (leader)endpoint and further inferring a mutual goal to affirmatively resonateon rewards and/or profits.

The system may infer a syndication factor and/or indicator associatedwith indicators on semantic identities and/or semantic groups.

When performing inferences, the system may counter bias factorizationsof syndicates and thus biasing for inclusion of the artifacts which areless and/or non-syndicated.

The system and/or other semantic entities may look to resonate, adhereand/or recruit semantic artifacts/identities during goal achievementand/or at semantic times. The artifacts which may benefit from the goalachievement may enter the resonant group; it is to be understood thatthe beneficial resonance may comprise for example higher budgets, lessunknowns and/or any other factorizations which determine, preserveand/or project such/similar factors and/or higher (projected)satisfaction, happiness and/or stimulation.

In further examples, in order to build resonance, the adherent and/orrecruited semantic artifacts/identities may be asked to enter intosemantic contracts/agreements, reserve budgets and/or commit budgetsinto an escrow; thus, such reserved and/or escrowed budgets are notavailable for further inferences at the particular entities. In someexamples, the contract and/or escrow is held by a broker, insuranceprovider, leader and/or any other entity as indicated by semanticanalysis and/or further semantic time.

The system may broker negotiation inferences wherein at least twoparties want to trade and/or access each other's (particular) publishedcapabilities and/or resonant artifacts. Each party's tradingcapabilities, costs/budgets and/or further deal indicators/factors areassessed by the system and further communicated to each party; if alltrading parties agree with the assessment/clauses, the parties pay abrokerage fee to the system and pursue with trading based on system'sassessment. It is to be understood that the system may be any artifactexplained in this application including semantic artifact, brokerage,insurer, collaborator, flux, device, module, post etc.

In further example, the trading assessment includes clauses to be metduring semantic times. As such, the brokerage fee may be associatedfurther with such semantic times and be paid when clauses are met.

In further examples, the system uses factorizations of ownership basedon (factorization of) semantic times to infer the liabilities. In someexamples, one semantic entity/collaborator hands over a challenge and/orfurther artifact to another semantic entity/collaborator. The system mayassess the proper hand-over through the factorization of semantic time(e.g. 60% JOHN GAVE JANE THE DELOREAN, DELOREAN BRAKES ARE BLOCKED);based on the contractual clauses and handover factorizations the system,broker and/or insurer may assess the liabilities which may occur duringhazardous and/or failed circumstances (e.g. JOHN may be liable becausethe brakes where supposed to be in good shape during the handover). Inother examples, the receiver may fail to properly receive and/or followthe receive clauses (e.g. JANE WAS READING HEALTH AFFAIRS WHILE JOHN WASDEMONSTRATING THE DELOREAN BRAKES etc.).

The system may use semantic analysis including the risk/fear ofloss/gain and/or other factors for managing connections, contractsand/or budgets. In some examples, the system disconnects, cancels,updates and/or challenges (e.g. for connections/clauses, budgets etc.).

Based on contractual evidence and/or clauses the system may challengeand/or be refunded for the budgets that have been spent on clausesbreached by other parties and/or collaborators. In further examples, thechallenges may be accepted immediately and/or be gated/routed to abroker and/or insurer for further feedback and/or semantic analysis. Itis to be understood that the challenges may include explanations,opinion and/or further analysis of challenger, challenged, broker and/orinsurer.

The system may implement, incorporate and/or comprise multiplemodalities in the same sensing and/or rendering capabilities. Multiplemodalities may be implemented using the same sensing and/or renderingentities and/or artifacts.

The system may implement, incorporate and/or comprise multiple sensing,rendering and/or modality capabilities.

The system may combine optical, electric, magnetic and/orelectromagnetic input/output capabilities.

Semantic technologies allow the combination and/or embedding ofoptical/light/electro (/)magnetic emitting and ingestion. In someexamples such capabilities are based on semantic cells, MOS (/)FET,CMOS, polariton, nano (pillars) entities and/or other sensing and/oraugmentation entities/capabilities as explained in this application.

Semantic display devices may incorporate and/or comprise camera and/ordisplay capabilities in a single housing and/or using the same (optical)sensing entities. In some examples, the sensing entities may be based onluminescent tunable polaritons. In other examples, they may be based onother fluorescent and/or phosphorescent entities.

The system may use (biometric) (semantic) identification (e.g. based onfingerprint, facial, gait, user/artifact characteristics etc.) using asemantic display and/or (further) camera based on and/or coupled withsemantic shaping and/or further semantic analysis to continuously (e.g.at every touch, inference etc.) and/or at interval of times (e.g. basedon semantic times etc.) identify and/or further authenticate users,entities and/or other artifacts. The semantic display may haveoptical/electromagnetic capture capabilities based onoptical/electromagnetic sensing and/or rendering. Such semantic displaydevices incorporate and/or comprise camera and/or display capabilitiesbased on optical sensing and/or rendering as explained in thisapplication. The semantic display devices may incorporate and/or allowany sensing and/or rendering capabilities and/or further modalities in acombined manner.

The system uses semantic analysis of fingerprint touch sensing onsemantic display surfaces. Alternatively, and/or in addition, the systemuses semantic analysis of other (biometric) semantic identificationcharacteristics.

The use of near to far field detection helps the system to understandthe views. We also explained the “earlier”/“sooner” vs “later” relatedinferences. We also explained that the system may blur, denoise,emphasize perform overlays based on the depth detection and/or (semanticmodel) hierarchy.

In some examples, a module is affixed to an entrance and/or observingand entrance; the entrance may comprise a door and/or may be affixed toa door. The module may comprise multiple cameras and/or vision elements.In an example, in intrinsic behavior, the vision output may be blurred,superposed, overlay-ed, captured at low resolutions and/or composed withother artifacts. In the case that the system infers that based onsemantic analysis it needs to understand the circumstances and/or videofeeds better (potentially overall and/or only at various endpoints) thenit may further increase the resolution, decrease blurring, adjusts theoverlays and/or condition the generated noise. In an example, the systemdetects movement, obturation and/or holding of the door handle and thusit may project that somebody wants to enter and/or exit; thus, thesystem may further activate regions and/or endpoints in order tounderstand the situation. In some cases, the system detects that thedoor handle is hold and/or operated by a human hand and thus it mayactivate the capabilities, endpoints and/or areas associated with(semantic) identification, authentications and/or biometric recognition(e.g. endpoints mapped to body, face and/or other biometricdiscriminator endpoints for detecting facial, gait, stance, fingerprint,identity etc.). It is to be understood that while performing suchanalysis the module may blur and/or unblur the endpoints in successionand thus at no point in time the system and/or module captures the wholescene unblurred. The system may further perform semantic analysis basedon the movement direction (e.g. of door open/close, person etc.),projections, semantic access control and/or further analysis todetermine the necessary actions (e.g. deactivate and/or invalidate dooropening sensor outputs, actions, commands, access control, notificationsetc.). The system may detect other hostile and/or hazardous conditionssuch as door/glass breaking/fire/smoke (e.g. by the hostility,offensiveness, diffusion, orientation and/or hazard of detected semanticidentities, foes, glass pieces, flames, clouds, ionizations) etc.

In some cases the users may setup distress (anomalous, hazardous etc.)coded behaviors (e.g. hold the door handle in a certain way, tap in aparticular way, shake the head in particular ways etc.) and thus thesystem may further perform analysis based on such behaviors.

The system may select areas and/or endpoints (e.g. on a (semanticdisplay) surface) based on (wearable) sensing and/or further projectionsof mappings to the endpoints and/or their associated artifacts. In anexample, a user wearing a glove and/or sensors attached to thumbnailand/or index finger collimate and/or project the observing endpointand/or further semantic views to an area on a display; it is to beunderstood that the collimation may be based on the observing endpointof a user eyesight, a camera, (wearable) optical device and/or any othersensor and/or renderer. The system selects the area on the screen basedon the inference of the (semantic) shape selected/determined by the(wearable) sensors and depth detection to the observing and/or viewingendpoints comprising the projection and/or mapped area on the viewingsurface. It is to be understood that the (wearable) sensing capabilitiesmay comprise any modalities explained in this application; in someexamples, the system uses optical/microwave/inertial (wearable) sensorsto sense the field.

Once the user selects the areas and/or endpoints it may use them infurther analysis, renderings, positioning, sensing, instructions,diffusion/propagations (e.g. of sound, electromagnetic etc.), accesscontrolled (allowed/disallowed) areas and/or any other inferences,transformations and/or commands.

User interface controls may comprise color mappings based on semanticanalysis. In further examples, the system sets up and/or command otherrendering and/or displaying parameters (e.g. brightness, contrast,resolution, viewing/rendering angle, projected viewport/display sizeetc.).

The system composes at least two user interface controls, regions and/orgraphs wherein the semantic composition and/or analysis takes place onthe parameters, characteristics and/or data of such controls, regionsand/or graphs. In some examples, the system may not allow the placement,composition and/or selection of controls and/or graphs if the semanticrules and/or routes won't allow it and/or the composite inferences areincoherent, confused and/or do not make sense.

When composing artifacts, the system may associate the composedparameters, characteristics and/or data to the new composite semanticartifacts and/or semantic identities. As explained, the user interfacecontrols may be associated with semantic network model artifacts andfurther accepting, allowing, denying and/or requesting user actions,selections and/or feedback.

The system may generate, determine and/or comprise opinion and/oranalysis in a hierarchical manner wherein the opinion/analysis maycomprise other opinion and/or analysis. The system may preserve semantictrails of such opinions and/or analysis and further render them viasemantic augmentation. In some examples the system renders the trails,hierarchies and/or further groups in a document, page, ui control etc.

The system may express opinions and/or analysis on artifacts (e.g.posts, users, semantic artifacts etc.) performances, health, consumedbudgets, learning, indicators and/or further factors, graphs, curves,rates, semantic displays, fluxes, streams, multimedia, articles and/orother artifacts.

The systems may explain to each other the signals and/or expressopinion/analysis on signals; such explanations may comprise semantictimes and/or other semantic artifacts. As such, when receiving thesignals from the collaborator, the system may condition the receivedsignals with a semantic wave generated based on the explanatory and/oropinion/analysis artifacts; if the system determines high confusionand/or incoherency then it may instruct the collaborator to adjust thetransmitted signals.

At least one collaborator may comprise multiple signal generation unitswhich may transmit the signal and/or various components of the signal.In some examples, semantic units coupled toelectromagnetic/optical/sound/pressure smarttransmitter/actuator/transceiver conditions, duplicate, splits and/orsends (at the same time and/or particular (semantic) times) at least twosignals (e.g. semantic wave, frequency band signals etc.) to particulardirections, links, trajectories and/or endpoints; as such, the systemreceives such transmissions and conditions them based on theexplanatory/opinion artifacts. If the system detects confusion and/orincoherency then the system may instruct the collaborator/s to adjustthe signals (e.g. index, bias and/or adjust based on semantic artifactsand/or semantic time) until they increase coherency, reduce confusion,increase likeability/stimulation/affirmative resonance and/or achievefurther goals.

The system may consider multiple collaborators' signals,explanations/opinions and/or semantic groups thereof. Further, thesystem may consider the coherency, confusion, likeability, stimulationand/or resonance on a composite semantic group basis whether includingself or not.

The system may synchronize multiple streams/fluxes based on semantictime.

The system may use and/or generate compositions with and/or betweenartifacts, streams and/or fluxes. Such compositions may be conditioned,augmented and/or further synchronized based on semantic time management.In some examples, the system may compose streams comprising at least onevideo encoding and/or sound encoding (e.g. in various languages, voicesetc.); further, the system infers and/or is instructed to activate onelanguage over the other.

In further examples, the system composes two streams/fluxes based onsemantic time and/or further condition/gate a stream when high entropyand/or distortion is inferred. In some examples the systemrenders/augments a movie for a Spanish/English speaking entity and whenthe streaming sound in Spanish starts it may mute/gate the sound streamin English and activate Spanish; further, if distortion occur betweenEnglish to Spanish translation artifacts the system may mute/gateSpanish and activate English.

The system redirects conditioned, split and/or duplicated signals toparticular entities and/or collaborators based on publishedcapabilities. In some examples, based on published, registered and/orexplained capabilities (e.g. operating interval, (frequency) responserange/cutoff/saturation), the system redirects signals with particularfrequencies to particular transceivers, transducers, actuators,amplifiers and/or speakers for optimal augmentation and/orinterpretation.

It is to be understood that the published, registered and/or explainedinterfaces may comprise operation intervals and/or response rangescomprising and/or reflecting saturation, hysteresis, damping, cutoff,resonance, temperature ranges, diffusion ranges, depletion ranges,resonance ranges and/or further semantic times.

The system may achieve goals on semantic group and/or further leadershipbasis. The system may project inferences based on various routes and/orleaderships.

The system may consider location and/or further circumstances in orderto adjust signals and/or explanatory interfaces. In some examples, thesystem knows that semantic posts BY THE WINDOW may have increasedmicrowave/sound signal penetration than other parts of a conference room(e.g. based on proximity to the window, repeater, collaborator, noisesource etc.) and it may consider to affirmatively/non-affirmativelycondition signal and/or semantic waves based on particularcircumstances.

In further examples, the system infers the optimal placement and/ormovement in a warehouse, store, area, facility and/or (virtual)environment.

The system may infer the optimal shipping routes and/or providers basedon semantic analysis.

The system enables/disables, adjusts and/or orients augmentationcapabilities based on semantic analysis and inference. Further, it mayenable/disable particular augmentation capabilities based on a usercircumstance, location, semantic time and/or profile (e.g. if usersand/or semantic groups of users have a status which are highly entropicwith receiving the augmentation (on particular devices) the system maynegatively factorize and/or disable the augmentation (on particulardevices); analogously, if the users have a status which are un-entropicwith the semantic augmentation the system may positively factorizeand/or enable augmentation (on particular devices).

The system orients and/or adjust actuators, I/O, transducers, sensorsand signal orientation and/or parameters based on semantic analysis. Insome examples, the location and/or associated semantics of theaugmentation capabilities are inferred and used to determine optimaland/or believable inferences (e.g. the system may infer in a multimediastream that signal/sound/video snippets are associated with amotorcyclist traveling in a particular direction and/or trajectory andthus further route, gate, transduce and/or actuate the sound of themotorcycle to particular endpoints and/or associated elements (e.g.displays, speakers) which will allow the display and/or sound effects totake place as detected in the multimedia stream (e.g. in rapport with aviewer, user and/or observer). It is to be understood that the systemmay detect via a primary multimedia embedded modality (e.g. videoartifact) the direction and/or trajectory of the motorcycle in rapportwith the observer (e.g. recording camera) and further infer particularsecondary modality elements, artifacts and/or capabilities (e.g. soundgenerators, speakers etc.) which can augment using a similar (e.g. lowentropy, drift, shift) trajectory (of the secondary modality medium—e.g.sound), orientation and/or semantic times. In further examples, thesystem generates the secondary modality augmentation signals and/orsemantic waves based on the inference on the primary modality.

The system infers and/or projects the propagation and/or diffusion ofmodalities mediums (e.g. electric, magnetic, electromagnetic, pressure,(ultra)sound, chemical, biological etc.) mapped in the semantic networkmodel. In some examples, such mediums are associated withsemiconductors, solids, air, liquids and/or other environments.

The modalities may be based on streams, fluxes and/or recorded/embeddedin multimedia artifacts.

The system may infer particular modalities based on specific multimediaformats, encoders and/or codecs.

Multimedia artifacts may be stored on multiple devices and/or memories.

In some examples, the system stores various layers of multimediaartifacts and/or semantic waves on different memories, hierarchies,levels, devices and/or semantic groups thereof. As such, the multimediaartifacts may be coherent composed, collapsed and/or rendered only bythe composition of the artifacts on semantic group basis. Further,access control and/or gating rules allow only particular entities tocoherently compose the multimedia artifacts and/or parts thereof.

In some examples, the system comprises budgets and/or further semantictimes for storing and/or disposing multimedia artifacts; further, thebudgets are specified on a sematic time basis, semantic views and/orhierarchy (levels) (of memory, semantic network model, semantic groupetc.). For inference, storage and/or retrieval of a semantic artifactthe system may infer, project, store and/or retrieve a plurality ofsemantic routes/trails which determine coherent and/or less confusedinferences in rapport with the semantic artifact. The system useschallenges based on leadership semantics associated with the artifact inorder to infer, project store and/or retrieve such coherent semanticroutes/trails and/or further the artifact; it is to be understood thatsuch challenges may be within its own (semantic) memory and/or of itscollaborators.

The system may infer an expiration and/or budget on retaining andfurther purging particular inferred and/or stored artifacts in memoryand/or disk; further, the system may use likeability, desirabilityand/or further factorizations to keep and/or validate the artifactsand/or further (by H/ENT) it may use undesirability factorizationsand/or decaying for invalidating and/or expiring them.

In further examples, the system provides directions and/or furtherprojections towards achieving a goal, an endpoint and/or a destinationfor a user and/or semantic post; thus, it instructs the user and/or postto turn to and/or to follow a particular door, direction, landmark,street, post, person, leader, device and/or any other artifact. However,if the instructions are ignored repeatedly, after a number of timesand/or based on a semantic budget and/or (further) semantic time thesystem may ask the user and/or semantic post about feedback on why itdoesn't follow the instructions; alternatively, or in addition, thesystem may ask for a new goal and/or destination and/or it cancels,invalidates, turns OFF, STOPs, ENDs, BLOCKs the instructions for thecurrent goal and/or destination (based on semantic time). It is to beunderstood that the system may cancel the instructions based on anyinferred circumstances and/or based on the user feedback. Analogously,the system may factorize and/or decay any multimedia artifactsassociated with the projections and/or directions.

As previously mentioned, the system may factorize preferences and/orfurther routes based on sensing and/or inputs from the user. In anexample, the system augments the user with a multimedia artifact andfurther factorizes likeability based on inferred actions from a user(e.g. infers that the user likes a song because the user turns thevolume up as inferred by a semantic (display) unit and/or sound sensor;infers likeability based on applauses, collimation, field of view,endpoint action and/or further associated semantics, location etc.).

The system may determine, validate and/or render pointers/tags on ascreen based on semantics inferred based on user inputs to particularlocations and/or endpoints wherein the system doesn't render thepointers/tags unless the semantics are properly factorized and/orrealized.

The semantic posts may be used in various configurations and/or usecases. In some examples, the posts are deployed at particular endpointsand/or locations based on different needs.

The semantic posts may be deployed in hazardous and/or restrictedconditions and/or behaviors. In some examples, they are used todisinfect various areas, zones and/or endpoints and further control theaccess to such areas (e.g. by commanding through precise beaming theopening/closing of such areas before and/or after disinfection, bybeaming electromagnetic energy generated by semantic groups, byactuating and/or manipulating cleaning substances sprayers etc.).

In some examples, restrictions are represented based on semantic gating;further, the system may be able to tune and/or diffuse the restrictionsbased on such semantic gating.

We explained the use of grippers, latches, locks, bases and othermanipulation and/or hooking components. It is to be understood that suchcomponents may be attached to dampers and/or springs and thus providingsuspension and/or adaptive support for the transportable and/or lockablecarriers and/or cargo.

The semantic posts modules may comprise rotating platforms,sub-assemblies and/or parts which allow the modules components (e.g.locks, hooks, arms etc.) to rotate and such orient to desired positions.In some examples, such sub-assemblies (including or excluding electricalmotors) are affixed, locked and/or rotate around a first semantic post;further, the sub-assemblies comprise a fixed part which lock on thesemantic post and a circular motion part which rotate around the fixedpart to a desired position. In other examples, the fixed part comprisesa circular electric motor; alternatively, or in addition, the electricmotor is coupled to the semantic post and actuates the post, segments ofit and/or further the circular moving sub-assembly.

The semantic posts comprise one or more arms. In some examples, at leastsome of the arms may be used and/or coupled to supporting artifactsincluding other posts artifacts. The support may be used to enhanceand/or augment its stability by being positioned and/or affixed toinferred endpoints and/or locations on floors, walls, doors, posts,carrier and/or any other physical artifacts which are considered by thesystem during inference.

The arms may handle, couple, connect and/or grip (to) various toolsrequired to accomplish missions. Once the arms couple to the tools (e.g.scissors) then the system may need to understand the force, capabilityand/or actuation for using such tools. As such, the system may beprovided, read, waved and/or explained the actuation capability of thetools; the semantic coupling may be achieved via semantic gating.

The system may attach various fairings and/or body parts to the semanticposts, wagons and/or cargo in order to increase and/or model theaerodynamics and/or appearance of the ensemble. In some examples, thosecomponents are inferred based on the mission and/or furthercircumstances comprising weather conditions, route/ride characteristics,maximum speed/acceleration, fuel/charge depletion rate, noiseprotection, passenger profiles, preferences and so on.

The system may ensemble a composite vehicle based on semantic profilesand/or user/passenger preferences. In some examples, JOHN specifies thathe wants and ensemble vehicle to have sporty characteristics and/or belike a DELOREAN. In further examples, JANE specifies that she wants avehicle which will maximizes comfort, lowers noise and looks like abeetle. It is to be observed that if JOHN and JANE need to travel in thesame carrier there are some composite requirements which may becontradictory (e.g. a DELOREAN may not have the maximum comfort however,the system may be able to provide a more comfortable or sporty ridebased on adaptive damper adjustment and/or spring (pre) loads, a beetleshape is more round while a DELOREAN is more squared etc.). When thecomposite requirements cannot be satisfied (e.g. due to confusion, no(borderline) resonance on leadership and/or all requirements etc.) thesystem may further ask user/passenger/fluxes on choices etc.

The system may use semantic shaping and/o analysis to determine shapeand/or composition of vehicle ensembles.

The system may ensemble fairings on the posts, carriers and/or cargousing lockable/hooking mechanisms (e.g. such as the ones explained inthis application). Further, the system may use its own manipulationcapabilities and/or other entities manipulation capabilities forensemble and/or attachment of such components.

The fairings may comprise multiple lockable and/or damped layers (e.g.embedding locks/hooks/grips, dampers, springs etc.). The fairings and/orfurther layers may be manufactured from any material including but notlimited to plexiglass, ceramic, plastics, polycarbonate, rubber, carbonfiber, steel, aluminum, titanium and/or meshes.

The fairings and/or layers may be connected to an ensemble and/orbetween them through locks/hooks/grips, dampers, springs and/or furtherlinkages.

During (projected) crashes the system may adjust (e.g. viadamping/hysteresis/indexing etc.) the damping and/or linkage/spring loadbetween the fairing layers in order to absorb the crash shocks. It is tobe understood that the system may consider also the material shockabsorption and/or deformation capabilities when inferring the composedcharacteristics.

The system may use semantic network models mapped to a (projected) crashscene and/or hot/hazard points (e.g. crash endpoint, contact points onthe fairings etc.).

The fairings may comprise and/or embed sensing elements.

The fairings and/or further layer may expand and/or retract based oncircumstances and/or further semantic analysis. In some examples, thesystem expands particular fairings in order to increase the drag. Infurther examples, the system expands/retracts the fairings in order toadjust air flow (e.g. for fuel cell, battery and/or cockpitcooling/heating) and/or further tire (/) road feedback/pressure, weightand/or turning characteristics. It is to be understood that the systemuses environmental conditions (e.g. outside/inside temperature,pressure, wind etc.) in order to determine air flow and/or further drag.

The system may adjust the fairings, linkages and/or attachmentlinkages/locks for increased leverage and/or protection during(projected) hazardous conditions and/or crashes. In some examples, thesystem projects a frontal crash and thus it extends/retracts,stiffens/weakens particular fairings, layers, articulations,locks/hooks, linkages, arms and/or further parts in order to mitigatethe effects of the crash by damping, managing hysteresis, deformationand/or further absorption of the effects (e.g. shock, crash energy,deformation, deceleration, hot/hazard endpoints etc.) and/or to furtherprotect the passengers/cargo. It is to be understood that the system mayuse gating, diffusion and/or further semantic analysis in order toproject and/or propagate the hazard inferences to particular endpoints(e.g. passengers, cargo etc.) from the hot/hazard crash (contact)endpoints.

In further examples, the system determines (projected) encounters (e.g.of curbs, holes and/or further obstacles) which require adjustableclearance and thus, the system tunes the fairings, damping/rebound,spring (pre) load to retract and/or to adjust and accommodate suchconditions. It is to be understood that such conditions may be inferredby sensing (e.g. camera, accelerometer, inertial etc.), locationcharacteristics and/or further semantic flux. In some examples, thesystem receives by flux that at a particular location there is a steepledge which may be hazardous and have specific characteristics (asexplained and/or measured by sensing at the flux collaborators) and assuch the system projects the encounter and uses further detection byoptical processing and/or acceleration of wheels up/down the ledge toproject/detect the ledge encounter and/or mitigation activity; further,the system may retract the fairing before reaching the ledge as inferredand/or specified based on semantic time, fairing adjusters hysteresisand/or further mitigation rules/routes.

The system uses information from the ensemble locks/gripper sensors inorder to detect conditions and/or adjust fairings. In some examples, thesystem determines that the locks on the fairings bear too muchpressure/shear stress and/or can become hazardous in particular windyconditions and/or when fairing is extended/retracted. In furtherexamples, the system may use another fairing adjustment to alleviate thestress on the potential hazardous and/or locks. The system may considerand/or adjust speed, acceleration, tire pressure, drag, environmentalconditions and/or further projected circumstances.

The system may project whether parts were installed or not installedproperly based on sensing (e.g. detectingload/pressure/movement/acceleration/wiggle) and/or further opticalinspection (e.g. via camera/infrared etc.).

In further examples, the system captures videos or pictures of(potential anomalous/hazardous/hostile) installations.

The system, broker and/or insurer may infer, ask and/or be provided withopinions and/or analysis on installations, storage, manipulation,reliability, expected asset performance, maintenance (history) and/orfurther suitability for purpose. Such opinions and/or analysis may beshared based on semantic flux/gating.

The system may further provide the (insurer) flux network with bill ofmaterials (BOMs), maintenance/storage records/trails, opinions/analysis,designed purpose and/or further passenger profiles, preferences and/orgoals/missions.

Insurers may bid on insuring particular trips, carriers, cargos,ensemble vehicles, users and/or passengers. In some examples, thesystem, users and/or passengers may have, select and/or infer a selectedpool/group of insurance providers (for particular semantic profiles,cargo, ensemble, missions and/or goals).

Storage and/or manipulation circumstances and/or further trails may beconsidered during insurance inferences. In some examples, hazardousstorage conditions and/or manipulation may determine factorization ofhazard and/or risk.

The system, broker and/or insurers may determine factorization of hazardand/or risk at asset, semantic identity and/or mission level.

The system, broker and/or insurers may index/factorize premiums based onsemantic analysis (e.g. increase/factorize premiums for higher hazardensemble, mission etc.).

The system may map endpoints to storage and/or other compartments andtrack ingestion and/or removal of items.

The system may comprise areas, providers and/or associated devices whereposts, devices, vehicles and/or other artifacts are maintained, storedand/or repaired. Further storages/memories associated with those areas,providers and/or posts/devices may allow an user, owner, leader and/orother entity to leave and/or transfer the security keys (e.g. (DNAchains) semantic network model, key, fob, rf identification keys,digital key, public key, private key etc.) allowing the starting and/oraccess to such devices/artifacts. In further examples, a userleaves/transfers the security key to the particular providers, storagesand/or memories; even further, the system may specify the validity ofsuch security keys based on semantic times. The user may specify thereason and/or opinion on the posts/artifact entering themaintenance/repair area and/or why the security key has been droppedoff. In some examples, the keys comprise a security key transferredbased on radio frequency and/or optical codes and/or protocols.

We explained the use of tenses for determining the ordering ofinferences, updates and/or learning. It is to be understood that thesystem may keep the semantic trails of past inferences, leanings and/orupdates and as such it may determine the semantic time of the inferencesand/or leanings (e.g. I DIDN'T KNOW THAT JANE WAS READING HEALTH AFFAIRSBEFORE CONNECTING WITH JOHN FOR DINNER, I FOUND LATER THAT JOHN ISCONNECTING WITH JANE FOR DINNER etc.).

The system keeps the information up to date and further managesnotifications and/or commands based on semantic inference (e.g. notifyme when John appears, notify me when John disappears, augment me whenJohn appears or when Jane informs me about John's whereabouts, augmentJohn with my messages when you find him, augment me with John'sappearance, clothing, car, messages etc.).

The system may determine an unhappiness/sadness factor based on thefurther loss and/or distancing of a likeable and/or highly affirmativeresonant artifact in current and/or further projections wherein theprojection of gaining the artifact and/or further resonance are unlikelyand/or not possible (in a semantic time).

The system may infer unhappiness/sadness based on the sudden loss ofhighly factorized affirmative resonances and/or associated artifacts(e.g. routes) wherein there is no further possible routes for gain an/orfurther resonance in a semantic time; in some examples, the semanticidentity associated with the resonance disappears, expires and/or isinvalidated and the intrinsic behavior of such entity is marked as such.Further, the unhappiness factor is based on regrets in rapport with thehostility oriented towards affirmative resonances and/or blocking ofaffirmative resonances.

During semantic learning from particular entities/leaders/artifacts thesystem may determine low drift, shift and/or orientation towardscore/hard DO NOT/BLOCKED rules. When this occurs, the system may infercircumspection factors in rapport with particularentities/leaders/artifacts. When circumspect the system may increase thesemantic spread and/or decay affirmative resonance with the particularentity.

The system may factorize unhappiness factors when circumspectioninferences, loss of resonances and/or factors increase despite thecounter measures (e.g. increase in semantic spread).

If the system infers VALIDATE/ENABLE/ON/ALLOW/DO/YES/ACTIVATE/START typeinferences and/or direct polarity for associated semantics and/orcompositions then for high drift, shift and/or entropy semantics maydetermine and/or associateINVALIDATE/DISABLE/OFF/BLOCK/DON'T/NO/NOT/CANCEL/STOP/END typeinferences and/or inverse polarity.

The system may infer enable/disable, activate/cancel, ON/OFF,ALLOW/BLOCK, DO/DON'T, YES/NO, START/END, START/STOP validate/invalidatetype inferences and/or actions; further the system may use suchinferences to determine whether an (associated) artifact and/or(associated) activity is in superposition, hysteresis, damping and/ordiffusion (e.g. when is between (the semantic superposition time of)ON/OFF, START/END, START/STOP etc.) and/or is realized/not-realized;when realized/not-realized, the system may gate and/or invalidaterelated superposition, damping, hysteresis and/or diffusion.

The system uses high inference entropy inferences to determine and/orinfer activities. In some examples, the system infers the start/onand/or stop/end/off of an activity, route and/or sub-route. In furtherexamples, the system infers the start of activity while rejecting and/orrerouting the inferred semantics which have high entropy in rapport withthe activity.

The system infers the end/stop of the activity based on the completionof a route, sub-route and/or expiration of budgets and/or semantic(superposition) times. Further the system may infer the realization ofthe activity based on inference of low entropy semantics in rapport withthe activity projections within the semantic superposition time; byH/ENT the system may infer the non-realization of the activity based onthe inference of high entropy semantics in rapport with the activityprojections within the semantic superposition time.

When a group of devices control the same artifact (e.g. analog, digital,switch, semantic artifact etc.) and if a member of the group (e.g.circumstantial leader) commands and/or switches the resource to onestate and/or further circumstance then the other members of the groupmay be in a high entropic (and/or H/ENT), highly distorted and/or out ofsynch condition of their current published semantics in rapport with theresource. When this happens, the system may instruct the highly entropicand/or out of synch members of the group to adjust their publishedsemantics in accordance with current state of the resource.

The system may perform DO/ALLOW and/or DO NOT/BLOCK augmentation. In anexample, the system instructs a carrier to not turn in a particulardirection because the environmental conditions (e.g. wind, fire etc.)may cause hazardous inferences to diffuse, spread and/or factorizeand/or affect artifacts including the carrier.

The system performs discrimination based on entropic inferences.Further, it may learn that high entropic semantics (associated withindicators, capabilities and/or behaviors) determine discriminationfactors for (composite) semantic identities and/or semantic groups. Insome examples, the system learns that one post has a hook (e.g. forminga semantic group) which connects to things and tows while another postdoes not have a hook and thus cannot connect and cannot tow; further,the system infers that discrimination is based on (H/ENT) whether thepost has or has not a hook/copter and further that the post istowing/lifting or not towing/lifting capable. Since the intrinsicbehavior of a post is to be non-towing, in order to discriminate thetowing post (or post with a hook), it adds the discrimination capabilityto the semantic identity or the semantic group of the (composite) post(e.g. post with hook, towing post). Further, the new intrinsic behaviorand/or capability is reflected by the discriminatory semantic identityand/or further artifacts (e.g. semantic routes, groups etc.).

The system intrinsic behavior is to project and factorize an affirmativeindicator/identity and to decay a non-affirmative indicator/identity.

We mentioned that the system may implement fight or flight inferences.The system may fight when the inferences, projections and/orconsequences on foes actions determine highly factorizeddissatisfaction, unhappiness and/or high risk.

Challenges by (competing/critic) participants in the semanticflux/stream network may try to decay the affirmative semanticindicators/capabilities/identities and/or factorize non-affirmativesemantic indicators/capabilities/identities. When challenges are hostilethe system may infer foe and/or bullying factors and/or semantic groupsand further implement fight or flight inferences. By H/ENT, thechallenges and/or participants which resonate with the intrinsicbehavior and further counteract bullying may increase likeability and/orfriendliness.

The system may factorize anxiety when bullied and/or when its(published) semantic identity and capabilities are threatened in thesemantic network.

The system may infer that the time passes slower (and/or furtherfactorize “later” vs “earlier” type inferences) when the anxiety ishigh.

The system may strive and/or project to achieve/induce and/or furthermaintain particular semantic identities in the semantic network withinparticular semantic groups and/or with/for self.

The system may assign particular leadership semantics and/or semanticidentities for semantic times and/or intervals in sematic trails and/orroutes.

The fight or flight responses may be based on the risk of loss, risk ofgain, reward of loss and/or reward of gain. In some examples, the systemis biased to fight when the risk of loss and/or reward of gain ofaffirmative semantic artifacts, (self) identities and/or resonances ishigh; further, is biased to fight when the risk of gain and/or thereward of loss of non-affirmative semantic artifacts, (self) identitiesand/or resonances is high. Analogously, by H/ENT on fight/flight, thesystem is biased to flight when risk of loss and/or reward of gain ofaffirmative semantic artifacts, (self) identities and/or resonances islow; further, is biased to flight when the risk of gain and/or thereward of loss of non-affirmative semantic artifacts, (self) identitiesand/or resonances is low.

The system is biased to fight when its highly affirmative semanticidentities and/or high investment artifacts (e.g. artifacts whichrequired high budgets for inferences and/or achievement) are threatened.Further, by WENT with affirmative inferences, the system is biased toflight when its low-affirmative/non-affirmative semantic identitiesand/or low/null investment artifacts are under threat.

The semantic posts may incorporate copter modules including rotorcraftin which lift and thrust are supplied by horizontally-spinning rotorsand/or motors.

The semantic analysis including overshoot/undershoot and furtherrotor/motor control may be applied to project/infer the localization.routing, bearing, speed, orientation (of the post, copter blade etc.),operating intervals, lift, thrust, altitudes of the flying copter-basedposts.

For overshoot and/or undershoot inferences the system may send alarms(e.g. to supervisors, owners, users, leaders, artifacts etc.), blockand/or invalidate semantic identities and/or artifacts associated withthe alarms.

The system may overshoot/undershoot the goal and damp it when budgetsare tight and/or is under pressure. In some examples, the system may beunder pressure and be factorized accordingly when the (projected)augmentation determine high consequences to itself and/or resonantcollaborators and/or budgets are tight. In further examples, thepressure is factorized when the system has high popularity (in rapportwith particular capabilities).

The system may divest/divert the challenges and/or further pressure toother (less popular) capabilities and/or collaborators. In furtherexamples, the system creates and/or publishes divestiture capabilities.Alternatively, or in addition, the system may progress more (semantictime) slowly toward the goal and thus decreasing theovershoot/undershoot, damping and/or hysteresis.

The system may diffuse likeable artifacts in order to increaseresonance.

The system may challenge the semantic flux network and/or collaboratorswith important/critical tasks and/or semantic budgets in order to buildresonance with the respective fluxes and/or collaborators. It is to beunderstood that the criticality of the task may be assessed based onevidence and/or further elimination of distortion; as such, the taskassessing entity may resonate when the distortion is low (e.g. on thelowest interval).

When connected and/or instructing systems to connect/entangle the systemmay infer the risks of losing the connection/entanglement.

The system may condition signals based on projections of signalcharacteristics expected from endpoints and/or paths.

In general, as explained, the system uses at least a first modality toaugment at least another modality.

The system may project, expect, (counter) bias, determine and/orcondition signals (e.g. distortion/noise/fading determined by multipath,dispersion, diffraction, scattering, Doppler shifts etc.) based on theobservations in the sematic field (e.g. observation, semantic times andcharacteristics of objects, collaborators, environment, communicationentities and/or further paths and/or obstructions to these entities.

In some examples, based on projections and/or factorizations of thesignal distortion and/or noise in a (direct/indirect) path the systemmay collapse signals from the paths and/or further not related withdirect line of sight. In further examples, the system may prefer some(sensing/inference) orientations, paths and/or further conditioningbased on semantic analysis. In further examples, the system usesfactorizations (e.g. risk, likeability, happiness etc.) of gain and/orloss of to determine sensing parameters, orientations and/or furthersignal gains and/or losses.

The system may perform gating of opinion, analysis and/or commentariesin semantic streams/fluxes and/or multimedia artifacts. In some examplesthe system is instructed to provide, stream, snippet and/or select onlythe actual (particular semantic identities) playing time in a footballgame; in other examples, the system is instructed to provide, snippet,stream, select and/or add particular semantic identities commentariesand/or further artifacts; it is to be understood that analogously thesystem may be instructed to extract particular artifacts.

The system redirects and/or store data in particular locations based onsemantic routing. In some examples, the data is routed toaccess-controlled memories based on whether comprises and/or allowsinference of personable identifiable information.

The system may invalidate particular affirmative resonances if theresonant collaborators repeatedly induce inferences which are blocked byhard semantic rules and/or routes.

Semantic factorization may be used for encryption/decryption ofmessages, documents and/or further artifacts.

The system may infer the private key of an artifact by series, routesand/or trails of compositions and/or factorizations of prime numbers andfurther comparison (by orientation, shift, drift, entropy etc.) and/orcollapse of the inferred factors with the public key of the artifactsand/or further channels/streams that needs to be decrypted. The systemmay infer private and/or symmetrical keys using the decryption of(public key) encrypted artifacts based on semantic factorization and/orfurther analysis. The system may use such techniques for communication,cyber, gating and/or further semantic analysis.

In further examples, the system decrypts encrypted messages and/oridentifies keys in the messages (e.g. symmetric keys etc.) by comparingthe public key (components) of a receiver/sender with a list of publickey (components) computed and/or processed a-priori based onmultiplications of series and/or (semantic) groups of prime numbers.

The system may infer, learn and/or factorize key risk, performanceand/or control indicators based on semantic analysis and/or furtherleadership inference. In some examples, the system infers thatparticular artifacts, views and/or scenes require performance indicatorssuch as fast processing, accuracy, cleanliness, long term storage;further, control indicators such as connectivity, follow safetyprotocols, testing, resonance, budgets may be inferred; even further,the system may infer that risk indicators may be based on loss of power,not following/break of safety protocols etc.

The system may synchronize multiple streams based on semantic time.

The system composes two streams/fluxes based on semantic time and/orfurther conditions/gate a stream when high entropy and/or distortion isinferred. In some examples the system renders/augments a movie for aSpanish/English speaking entity and when the streaming sound in Spanishstarts it may mute/gate the sound stream in English and activateSpanish; further, if distortion occur between English to Spanishtranslation artifacts the system may mute/gate Spanish and activateEnglish.

The system may gate and/or diffuse semantic artifacts between semanticviews. In some examples the system comprises views for viewing incidentsand/or associated artifacts; further, the system specifies the view,device and/or destination where to be informed and/or augmented atparticular semantic times.

The system may generate impactful and/or surprise advertisings. Thus, itmay look to generate reasonable to high resonant artifacts with thetarget audience and/or semantic identities when the projections ofresonant artifacts (within a semantic time) for the respective audienceand/or semantic identities is low and/or unknown environments are high.By H/ENT, the system may look to generate reasonable to borderlineresonant artifacts with the target audience and/or semantic identitieswhen the projections (within a semantic time) of resonant artifacts ishigh (for the respective audience and/or semantic identities). Thesystem publishes and/or diffuses semantics to fluxes and/or channelswhich by projections would not distort the composite meaning.

The composite artifacts may be distorted by addition of (counter)biases, noise, omission of facts, emphasizingsnippets/components/artifacts other than the highly factorized leadersand/or other high entropic techniques in rapport with the coherentinferences etc.

The distortion may occur due to biases and/or further semantic artifactsused in inferences.

When the system infers the distortion of the clauses and/or furthercomposite meaning it may infer a distortion factor associated with thecomponents, clauses, semantic identities, groups, routes and/or trailwhich determined and/or influenced the distortion. Further, based on thedistortion factor the system may infer hostility factors and/or furthercensorship factors of the distortion generated group in rapport with thedistortion-ed semantic components and/or identities.

It is to be understood that the distortion factors may be used todetermine signal distortion and/or further conditioning. Further, thesystem may assigns censorship factors to artifacts (e.g. components,devices, fluxes, collaborators, semantic artifacts etc.) which distortthe signal and/or parts thereof and further use those in semanticinferences (e.g. creates momentum vectors and/or diffusions with variousentropies etc.).

The system may express opinions/analysis on potentially distortedartifacts and/or further censorship. Further, it may gate, diffuseand/or cutoff opinions/analysis comprising distorted artifacts.

The system may increase the semantic spread and/or further challengecollaborators, sensing and/or fluxes, so it may assess facts that eitheraffirmatively factorize and/or non-affirmatively factorize thedistortion inferences.

The system may (be configured with a) model diffusion and/orattenuation. In some examples the system may comprise models of chargedparticles and/or electrolytes wherein the particles may move from thepositive endpoints to the negative endpoint and/or vice-versa until theentropy decreases and/or further attenuation (factor/s) increases. Suchsemantic analysis and/or further commands may be used in (biological)sensors/dispensers/actuators, signal attenuation/modulation and/orfurther (semantic) artifacts/analysis.

We mentioned the use of collimation techniques which allow the system toperform advanced selection, manipulation, analysis and/or commands. Infurther examples of collimation, the system manipulates a remote sensingdevice having an optical receiving element which detects the radiationand/or scattering from display viewing surfaces and further semanticnetwork model artifacts and/or movements and thus inferring the pointingarea on the screen. In some examples, the system determines that thecollimation target endpoint and/or area are in a centered endpoint ofthe receiving inferred observing field of view of the optical elementand/or mesh. Thus, the system highlights and/or select the artifacts onscreen and/or further associated endpoints and/or hierarchies at thecollimated location. Further, the system may use further manipulation ofthe remote sensing such as particular movements in order to manipulatethe collimated endpoints and/or further artifacts on the screen.

In further examples, the system uses screen collimation between at leasttwo devices (e.g. the main viewing device and the main control device).The main control device may be in some examples a wearable and/or mobiledevice. Analogously with the collimation techniques, the main controldevice may be used to collimate and/or zoom on allowed areas on the mainviewing screen; further, the user may select on the main control devicecollimated and/or enabled controls and thus interacting with the viewingdevice. The collimated area may be further inferred and/or renderedusing techniques such as those explained earlier based on sensing,wearables and/or optical collimation.

The collimation and/or further selection techniques may comprisesemantic shaping. A collimated/selected (semantic) shape may be inferredbased on collimation of a (semantic) shape, area and/or associatedendpoints determined by a first (semantic) group of sensors and furtherprojections (e.g. on the observing sensing entity and/or furtherendpoints, on the collimated display/surface/volume and/or furtherendpoints). The system may collimate a semantic shape to anothersemantic shape in a hierarchical manner.

In further examples, the semantic shaping is determined by thecollimation of a shape determined by user hand gestures which compriseshaping of at least two fingers to encompass the shape and/or endpointof the collimated object. Alternatively, or in addition, the system maytap the at least two fingers in a way that the tapping point/endpointand further projection of the point/endpoint from the observing entityto the selection/projection surface collimate on the object/control tobe selected and/or associated endpoints (e.g. within and/or of theobject/control). Further (composite) gestures techniques such asgrabbing/gripping/holding may be inferred and/or used; it is to beobserved that such techniques may be inferred based on finger tappingand/or further compositions between them.

The system may infer a configured selection gesture by analyzing the UPImovement in the semantic field within a semantic time. Further, it mayassociate the gesture with the selection of collimated objects on thedisplay surface and further select the objects.

Configured selection gestures may be associated with UPI tapping,pushing, bending, waving, grabbing, moving and so on in particularsemantic times.

In some examples, the system uses configured and/or learned movements,hysteresis, damping, indexing and/or semantic time to infer tappinggestures. As such, the system infers based on object/UPI orientationmovement mapping and inference including stopping/forward/reverse,further damping, hysteresis and/or further attached sensors that thestoppage endpoint may be a tapping endpoint and/or a ending and/orstarting endpoint of a gesture and/or activity. Further, the system mayuse the hysteresis and/or damping of the UPI movement to furtherfactorize the affirmative/non-affirmative indicators/factors in relationwith the (tapping) gesture. If the system detects the stopping, contactand/or the composition between two UPIs at a contact endpoint the systemmay further detect the tapping, a further semantic group and/or acomposite semantic. It is to be understood that in the case of tappingthe system may not infer that a composite semantic identity and/orsemantic group is assembled because the diffusion at the tapping and/orassembly endpoint is minimal, non-existent, not enabled and/or notpossible.

In some examples, the user pointer indicator is the index finger.Further, at least two phalanges may have each attached, via at least onewearable, accelerometer/s and/or gyroscope/s such as they measure theorientation of phalanges in rapport with each other and further,potentially using orientation sensed by a lens and/or camera, with theenvironment. Thus, the system may be able to infer whether the finger isstraight, bended and/or further points and/or moves in particulardirections/orientations and further projects to particular endpoints.The system may infer the finger is straight and oriented toward adisplay surface and as such it may select a user interface object on thedisplay surface and further allow the user to interact with the displaysurface via additional gestures and/or movement. In some examples, theuser moves the finger in order to move the selection and/or focus fromone user interface object to another and/or select a plurality ofobjects; once the desired object/s are focused/selected the user mayperform further gestures to start activities published by the selectedobjects and/or semantic groups thereof. It is to be understood that theappearance and/or activities of the selected objects may be composedand/or collapsed (e.g. into a single object) and thus the system startsa composite activity of the composed object.

The system may detect that a finger/UPI/arm/limb is (almost) straight orbended by measuring the differences in acceleration, velocity and/orangles on multiple (relative and/or absolute) axes from the sensorsattached to each of at last two of its segments/components (e.g.phalanges for a finger; hand, arm and/or forearm; tight and calf for aleg etc.); as such, when the phalanges/segments/components are alignedthe system may detect that those have little difference and/or furtherlittle drift/shift and/or entropy from one another on particular(absolute) axes. It is to be understood that the system may calculatethe differences on axes by translating the sensor measurements which maybe on their particular relative reference systems and/or axesorientation to an absolute reference system and/or axes orientation.Further, the system may detect other particular gestures such aswiggle/bend/tap twice or multiple times etc. which may be composed toinferences, sentiments and/or commands (e.g. activate/deactivate tvcontrol, select, start/end activity, on/off, zoom, in/out, little, fast,slow etc.). While such inferences may be based on particular sensors isit to be understood that alternatively, or in addition, they may bebased on other modalities explained in this application (e.g. forimage/video capture etc.).

Once collimated, focused and/or selected, an object is marked on theprojection and/or display surface by various techniques includinghighlighting, contouring, coloring and/or other techniques. The systemmay collimate, focus and/or select multiple objects and/or semanticgroups thereof based on particular configured gestures within a semantictime. Further, the user may erase the selections based on furtherconfigured gestures.

The selection gestures and/or further associated activities and/orcommands may comprise sentiment evaluation (e.g. MOVE FAST SIDE BY SIDE,TAP REASONABLE FAST TWICE etc.).

The system indicates artifacts by pointing and/or orienting UPIs towardsthe locations and/or endpoints associated with the artifacts.

In some examples, the UPIs may be associated and/or used in conjunctionwith remote surgery (post) arms and/or grippers.

The observing (sensing) entity can be any sensing entity, module and/orpost; in some examples, the observing entity is a wearable camera,glasses, contact lenses, (embedded) optical/microwave/terahertzmodules/antennas/meshes and/or any combination thereof.

The observing sensing entities (e.g. camera C1 and/or lens L1 etc.) mayperform user/wearer identification based on iris and/or further eyeanalysis. The iris and/or eye semantic analysis may be based of thecharacteristics, location and/or components of the iris, sclera, cornea,retina and/or further eye biological components (e.g. blood vessels,melanin etc.) and/or conditions. The system may use the identifieduser/wearer particular semantic profiles and/or preferences to adjustand/or personalize interactions and/or further inferences. Further, thesystem uses the user/wearer identification in order to perform semanticlearning and to adjust wearer's/user's semantic profiles.

The system may infer the starting and/or ending of the pointing and/orindicating activities based on movement, start/end of obturations and/orfurther circumstances. In some examples, the pointing and/or indicatingaffirmative inferences and/or indicators are factorized with thesemantic time in which the user points and/or obturates (e.g. within anendpoint, semantic interval etc.) in a particular (stable) orientation.

In further examples, observing sensing entities such as cameras, contactlenses, glasses and/or optical meshes infer the localization, mappingand/or positioning of the user head/eyes/irises/pupils within theirmapped surfaces. Thus, the user may use such inferences to furtherproject the observing field of view, orientations, obturations and/orselections on the projection and/or display surface.

In some examples, the system detects the iris/pupil movement, sizingand/or orientation in rapport with the enclosing and/or hierarchical(semantic) mappings (e.g. sclera, eyes, head etc.). Thus, potentially byusing further circumstances (e.g. environmental, of user, display etc.),the system may (semantically) project the inferred observing (semantic)field of view, inferences and/or semantic field onto a (semantic) mappeddisplay and/or manipulation surface.

In an example, as depicted in FIGS. 26 A and B, the system detects themapping of the iris within EP1 as mapped to the lens L1 at a first timeand the mapping to EP2 at a second time. Thus, the system maps thesemantic field of view to EP1 and/or EP2 and further uses the transitionand/or diffusion of the iris from EP1 to EP2 for semantic inference. Theiris movement may be detected via the lens L1 which may be associatedwith a camera, or may alternatively be a sensor configured to detectlight reflected from the eye, such as with a source IR1; the source IR1may generate electromagnetic and/or optical radiation in the infrared,terahertz and/or ultraviolet. In some examples, the source IR1 is anIR/NIR source at (near) infrared wavelengths. In other examples, the IR1radiation comprises wavelengths blocked by the human eye lens and/orcornea (e.g. 400 nm or less); in further examples, they comprise lightwith wavelengths associated with the wearers (predominant and/or leader)iris color. In some examples, the source IR1 and/or the lens or sensorare embedded in a wearable unit such as wearable lens and/or glasses.

Although, typically, the IR1 source may emit radiative patterns,alternatively, or in addition, it may be (associated with)(filtered/conditioned) ambient light.

The system modulates radiation and/or emissions and/or further detectsthe backscattered particles/energy/signal. In some examples, the systemdetermines, at first, (e.g. via sensing and/or other sources ofinformation) particularities of the wearer's eye (e.g. iris color,cornea/lens reflectance etc.); further, such characteristics may bebased on other circumstances such as detected/determined environmentaland/or health conditions (e.g. humid climate and/or hydration determinesmore reflectance due to water, eye dryness related conditions determinesless reflections etc.). The system may use the determined user/wearer(eye) characteristics and/or further conditions in order to properlymodulate the emissions/transmissions and/or interpret the (back)scatter(e.g. the user has dry eyes and blue iris the system may emit, conditionand/or filter/gate (e.g. allow) photons and/or signals with energy,frequency and/or wavelength on the (upper) range (e.g. ˜2.7 ev+, ˜650THz+, 480 nm−) of blue visible spectrum; thus, the (back)scatteredenergy and/or signal may comprise the modulated/spectra information ofthe user/wearer iris color as opposed to other regions of the eye. Thelens L1, camera C1, projection and/or display surface may incorporate(semantic) optical meshes.

Further, in FIG. 27, 28 the system observes the semantic field of viewand detects at the endpoint EPV (mapped to artifacts of L1, as in FIG.26A) the obturation within a (semantic) time by the pointer (UPI) UPI1of endpoint EPS mapped to the projection and/or display surface (PDS).Thus, the system infers that the user may have indicated the coherentand/or meaningful hierarchical endpoint EPOA comprising EPS and/orfurther indicating the object SO (e.g. a (DELOREAN) car; a user controlor a button in a user interface WUI etc.). It is to be understood thatthe system may infer that the user indicated object SO based on aleadership inference at endpoint EPOA; such leadership inferences may bebased on circumstances and/or further challenge-response. In someexamples, the EPOA comprises a (DELOREAN) car (and its components—e.g.door, hood, wheel, semantic post etc.) and further environmental objects(e.g. vegetation, sand etc.); however, based on circumstances the systeminfers that the user has selected the (whole) car. If the leadershipfactorization is not strong/high (e.g. in rapport with the leadershipchallengers) and/or coherent, then the system may further signal theconfusion and/or challenge the user; thus, the user may further pinpointthe selection (e.g. decreasing the projection endpoint EPS byincreasing/indexing the distance between the observing entity L1 andUPI1, by collimating with just one eye etc.). The system may factorizeconfusion when there is no clear leadership between the car, itscomponents or environmental artifacts at EPOA and/or associatedendpoints in the selection inferences.

While the preferred indication method may be for UPI1 to align with theiris orientation, lens and/or further center of field of view, in otherexamples such as in FIG. 30 the system observes in the field of view theorientation and/or direction of the pointer UPI1 and further projects itto the projection and/or display surface within the endpoint and/orprojection field of view of the iris and lens L1 when tracing thepointer direction to the projection and/or display surface and withinthe center of the field of view as the system follows the tracing (iris)movement towards the projection and/or display surface.

While in the depictions the EPS is comprised in the EPOA it is to beunderstood that in other examples EPS comprises EPOA. EPV and/orEPS/EPOA may be comprised in distinct hierarchical semantic layersand/or views.

The display surfaces may comprise (mounted) projectors,windshield/window, semi-transparent, televisions, other displays or anycombination thereof.

It is to be understood that all techniques explained in this applicationfor (semantic) display surfaces and/or meshes may be applied toprojection and/or display surfaces and vice-versa. Further, theprojection and/or display surfaces may be used to project and/or displayinformation from (distinct and/or particular) video projectors based ontechnologies such as DLP, LCD, LED, LCOS etc. In further examples, theprojection and/or display surfaces are televisions and/or monitors whichmay or may not incorporate semantic analysis capabilities. Theprojection and/or display surfaces may incorporate touch typeinterfaces.

The system may use projection and/or display surfaces to display andrender signals, feeds and/or semantic artifacts.

The projection and/or display surfaces and/or further artifacts may becollimated, composed, assembled and/or overlay-ed in a hierarchicalmanner.

The system may use the projections and/or inferences in FIGS. 26, 27 and28 based on an observing entity each for each eye (e.g. L1 and L2).Thus, if the system detects that both eyes are open then it projects theendpoints based on intersections between EPSs and/or EPOAs as projectedfrom each eye. The preferable manner is for the system to inferleadership for an eye whether specified by the user, its profile and/orinferred by the system. The user may open/close eyes and collimate theendpoints from either L1 or L2 and possible further sequences; thus, thesystem further fusion/analyze the inferences and reduce confusion and/orsuperposition.

The user may be more specific about the selections and/or indications byincreasing accuracy of indication and/or further mapping (e.g. bykeeping only one eye open and/or further increasing/indexing the UPI1distance in rapport with the observing entity L1; by discriminating theobject—the car, the car without the post, the car without wheels etc.).It is to be observed that the circumstances may comprise a semanticidentity, artifact and/or further narrative (e.g. car, car on the beachetc.); further, they may have particular components and/or furtherartifacts removed and/or invalidated (e.g. car without wheels, car onthe beach without wheels etc.).

The system may infer which eye is closed and/or which eye is open basedon semantic analysis.

The system may challenge the user to confirm/infirm and/or explain the(inferred) (semantic) fields of view (e.g. IS THE CAR ON THE BEACH, CARWITHOUT WHEELS?, HOOD OR DOOR?, DO YOU SEE THE CAR ON THE BEACH?, DO YOULIKE (S3P3)/(CAR ON THE BEACH)/(DELOREAN)?, WHAT DO YOU SEE?, WHERE'SYOUR HEAD?, WHAT DO YOU THINK OF (CAR ON THE BEACH) (DELOREAN)(BEHAVIOR) (APPEARANCE)? etc.). It is to be understood that the user mayexplain the actions implicitly and/or intrinsically with and/or withoutbeing challenged by the system.

The system may overlay the challenges on objects (e.g. car, itscomponents, wheels etc.) and/or associated endpoints where the confusionis high and/or factorization is not conclusive. In further examples, thesystem overlays (pop-up) user interface artifacts/dialogs on thecomponents and further endpoints allowing the user to validate orinvalidate the selection (e.g. by collimating on the overlay artifacts,YES/NO/ENABLE/DISABLE/ON/OFF buttons etc.).

In further examples, the system infers EYE OPEN/CLOSE, ON/OFF and/orfurther (START/END) BLINK activity by the detection of obturation ofsclera, iris, pupil, cornea and/or further eyeball components by the eyelids at (dependent) semantic (superposition) (hysteretic/damped) times;the system may know, learn, detect and/or infer thecharacteristics/colors/mappings of those components and/or furtherblinking activity behavior (e.g. superposition, hysteresis, damping,semantic times) in particular circumstances. Further, based on eyeblinking, iris/pupil/sclera/cornea obturation/movement and/or othersemantic analysis the system may detect dryness, drowsiness, sleepiness,alertness, focus, confusion, hazards and/or other conditions (e.g. for asupervisor, driver, patient, player, performer etc.); alternatively, orin addition, the system may infer the WENT semantic artifacts of suchconditions.

In the case that the system detects hazard and/or confusion it maychallenge the user and/or subject for feedback. In some cases, thesystem detects non-affirmative conditions of the supervisor/user and/orfurther decreased/decayed effectiveness of counter measures in rapportwith solving confusion, hazard and/or emergencies (e.g. because thesupervisor/user has dryness, drowsiness, sleepiness, lack of alertness,lack of focus and/or (temporary/permanent) impairment conditions); insuch circumstances, the system may factorize the inputs from thesupervisor/user accordingly (e.g. decays and/or factorize theaffirmative/non-affirmative trust/risk factors, indexing and/or biases,)at semantic times.

The system discovers leadership artifacts which need to be inferred, metand/or in possession in order to infer readiness.

During semantic times requiring critical and/or hard route procedures,in order to achieve readiness, the system may counter bias increased insemantic spreads which may divert it to other routes causing it to notfollow the procedure steps and/or semantic times.

In some examples, the system augments effectiveness inferences based ona supervisor/user circadian rhythm disruption/hazard which may befurther based on the localization of the supervisor/user traveling tovarious locations and/or time zones and further disruption of sleeppatterns.

We mentioned that the system may be under pressure and/or furtherdetermine (under) pressure indicators and/or factors. In some examples,the pressure indicators and/or factors may be used to activatemitigations, damping and further relieve pressure. In further examples,they may be used to actuate pressure regulators.

The (under) pressure inferences may increase dissatisfaction, concernand/or stress factors if not mitigated within their hysteretic and/ordamping interval.

When under pressure is high and dissatisfaction, concern and/or stressnon-affirmatively collapsed (e.g. against system goals) the system mayinfer a (supervisory) artifact and/or group lack of leadership and/orlack of coordination in the flux network.

Further, when under pressure the system may be biased to not thoroughlyfollow and/or deviate from routes, rules and/or procedures; thus, hardsemantic routes/rules and/or counter measures may be used,(re)factorized, activated and/or retrieved in order to counter bias andfurther for steering towards following/performing of (critical/required)activities; in some examples, such activities are used to inferreadiness factors. The system learns by associating and/or furtherstoring the current activities, hysteresis, damping, superpositionand/or readiness values/intervals with inferences and/or furtherleadership artifacts in the semantic field.

The system may project and/or determine particular leadershipactivities/artifacts/goals and/or further desirable interactions fortheir realization (e.g. getting/obtaining/accessing/inferringinformation, capabilities (from collaborators) etc.). Thus, in order tofocus, optimize budgets and/or relieve pressure the system may ignore,filter, mute and/or silence messages (e.g. email, posts, SMS, UPIsetc.), devices (e.g. mobile device, television, PDS, UPI devices etc.),artifacts and/or fluxes which are projected as non (affirmatively)contributing and/or influencing (significantly) the realization ofparticular activities/artifacts/goals. In some examples, the particularleadership activities/artifacts/goals may be determined based onbudgets; alternatively, and/or in addition, they may be determined basedon a predefined, predetermined and/or inferred leadership number (e.g.the four most critical activities etc.).

The system projects the leadership activities and further, the artifactsfor (successfully) readying and/or achieving them. As such, the systemmay affirmatively factorize such artifacts, retrieve them from long termstorage to short term storage and/or adjust the (expiration) semantictimes (e.g. based on readiness, achievement, success etc.).

The system uses affirmative and/or non-affirmative indicators towardsthe realization of goals and/or confirmation of projections/hypothesis.Thus, the non-realization of goals and/or refutation ofprojections/hypothesis may be indicated by WENT(indicators/factorizations) of the realization of goals indicatorsand/or confirmation of projections/hypothesis.

The system may project both, realization (or achievement/success etc.)and/or non-realization of desired goals; thus, it may determineleadership artifacts for such projections and take in considerationthat, in order to achieve the desired outcome, it may need toFOLLOW/PREFER/ALLOW the leadership artifacts for the realization ofgoals and to NOT FOLLOW/AVOID/NOT ALLOW/BLOCK the leadership artifactsfor the non-realization of goals.

The system may be biased to acquire/pursue capabilities, activitiesand/or readiness when they do not significantly impact budgets. Thesystem may counter bias such inferences based on a utility indicatorfactorized based on the impact and/or leadership of suchcapabilities/activities/readiness in (projected) have on realization of(strategic) goals.

Wearables, lenses and/or glasses may incorporate cameras and/or otheraugmentation capabilities for inferring/detecting/collimating user UPIs,activities and/or conditions and further implement counter measuresagainst hazardous consequences of such inferences (e.g. create display,sound and/or vibrational patterns to awaken the user etc.). Further,they may provide renderings, overlays, projections and/or augmentationto the user. It is to be understood that the same techniques used onprojection and/or display surfaces may be used to indicate and/ormanipulate objects in a room, outdoors and/or other environments. In anexample, at least one camera and/or lens observes the environment anduses and/or projects the UPI orientation in an environment towards theindicated area, volume and/or object; thus, the system infers that theprojection and/or display surface is based on and/or comprises theindicated circumstance area, volume and/or object. Further, the camera(or another camera) may encompass and/or provide video feeds/streams ofthe projected indicated area, volume and/or surface and thus allowingthe user to visualize the environment and/or further objectselections/manipulations.

In an example, in FIGS. 29 and/or 30 the camera C1 and/or lens L1observe the (approximate) orientation O1 of UPI1 and further projectsthe direction of the UPI1 to endpoint EPOA and/or further surface PDSwhere the endpoint and/or object OA is located. Thus, the system makesthe projection that the user may have indicated at least an object orcomponent from the surface PDS and/or potentially from the object OA.While the system projects the UPI pointing to EPOA it may useintermediary, anchor and/or reference endpoints (e.g. IPOA) projected,inferred in the field of view of C1 and/or L1 tracing the (approximate)direction and/or orientation O1 towards the EPOA. In some examples, thesystem uses the IPOA as an anchor endpoint while adjusting the field ofview of C1 and/or L1 and/or associated semantic views to encompassand/or move from UPI1 to OA/EPOA/PDS.

While the system may infer the semantic identities, projections,surfaces and/or endpoints based on (projected) indications of singleobjects and/or endpoints alternatively, or in addition, the systeminfers such semantic identities, projections, surfaces and/or endpointsbased on (projected) indications of (semantic) groups of objects and/orendpoints. The system may further use challenges to reduce confusionand/or further discriminate the indicated artifacts. In some examples,the user indicates towards the top of a wall fireplace/shelf comprising,supporting and/or encompassing multiple objects and the system mayfurther infer that the PDS should comprise and/or render the top of theshelf, the objects on it and/or further wall background. Further it maydetermine the leaders based on inferred circumstances and/or furtherchallenge the user to further point, collimate and/or explain whichobject, group and/or semantic identity at the top of the shelf ispointing at. It is to be understood that the objects may be people orany other artifacts which may be associated with temporary or permanentsemantic identities.

The physical object and the user interface object on the projection anddisplay surface may be represented as the same semantic identity and/orartifact in the physical-virtual environment. Alternatively, or inaddition, the physical object and user interface object on theprojection and display surface may be represented and/or associated withdifferent (composite and/or temporary) semantic identities and/orartifacts (e.g. the bottle on the fireplace, the projected bottle on thefireplace etc.).

The system may restock shelves using the semantic posts and/or semanticmanipulation using UPIs. In some examples, the system may (re)placeitems and/or restock them when the balance, likeability and/or budgetsat a particular location are decayed and/or to further increaselikeability.

The PDS may comprise a (capacitive/resistive) touch (screen) interfaceand the user may further specify selections (amongst a group of inferredand/or rendered artifacts) and/or augment the system based on touchgestures and/or selections.

The system may challenge and/or confirm with the user the projectionsurface, selection, focus and/or manipulation via video feeds/streams,voice and/or other modalities.

In some examples an observing camera and collimation lenses are embeddedinto a single unit.

In some examples, the system may use undershoot/overshoot inferences tofurther determine endpoints projections and/or selection.

It is to be understood that the user hand, arm, fingers,eyes/irises/pupils and/or further delimited areas may be mapped tosemantic artifacts.

The system may detect the user's goals by inference on userpointer/pointing indicators (aka UPI). The user pointing indicators maybe associated with hands, head, eyes, irises, pupils, fingers and/orfurther movement, fields of view and/or orientation. In some examples,the user pointing indicators may be associated with wearable sensors(e.g. attached to user pointing indicators; lenses; glasses; camerasetc.); in further examples, they may be associated with other devices.

The user pointer indicators may be used as indicators of the useraugmentation, trajectories, goals and/or feedback in the environmentand/or further circumstances.

The system may associate user pointer indicators with semanticindicators and/or vice-versa and further use them in semantic analysis.

The system may use user pointer indicators and further inferred pointingdirections/trajectories as orientations in the semantic field and/orsemantic analysis.

The system may update/refresh the indicated endpoints and/or furtherassociated artifacts on the display surface based on semantic analysisand/or semantic time associated with inferences on UPIs. In someexamples, the system updates/refreshes particular user interface objectsand/or artifacts in order to preserve coherency (e.g. within aninterval), increase likeability/desirability and/or decrease confusion.In further examples, the system may refresh particular scenes, views,layers and/or an entire projection and/or display model layers and/orsurface.

The system eliminates the boundaries between the physical and virtualenvironments by allowing users to manipulate objects in a consistent wayin the composed environment which is based on a fusion-ed (hierarchical)semantic network model. As such, the system may point, select, drag anddrop objects from the physical environment to projection and displaysurfaces and vice versa. In an example, the system indicates, drags anddrops a tea pot from the projection and display surface to the fireplaceshelf and thus the system may further retain the goal of having a/thetea pot on the fireplace shelf; in case that the tea pot is an existingobject in the environment (and/or relevant fluxes) the system mayfurther detect it and further move and/or track (e.g. by camera C1) thetea pot to the desired location by using semantic postsgrip/carry/manipulation/movement; further, if the tea pot is notavailable in the environment the system may (issue) order/purchase thedesired tea pot from the (flux) network and/or sites. While the examplehas been made of manipulating objects from the projection and/or displaysurface to the physical environment it is to be understood thatanalogously, the manipulation may occur in any combinationwithin/between objects and/or artifacts in the physical environmentand/or projection and/or display surface. (e.g. views within theprojection and/or display surface displays the first floor fireplace(shelf) room/environment and the second floor fireplace (shelf)room/environment and the user and system manipulate objects betweenenvironments/(shelves); the user and/or system may indicate the physicalobject (e.g. on the fireplace shelf) and further drags/grabs/grips/placeit to a physical and/or rendered table.

When rendering the environment the system may render the actualenvironment and/or the desired manipulated environment (e.g. renders thefireplace shelf with the tea pot even if the tea pot is not actuallyphysically there). The user may specify and/or augment the system withwhat kind of environment wants to render; alternatively, or in addition,the system seamlessly renders the environments based on inferencesand/or profiles; further, the system may augment the user on the type ofenvironment.

While a projection and/or display surface has been exemplified, it is tobe understood that the virtual environment may comprise multipleprojection and/or display surfaces. Further, each projection and/ordisplay surface may comprise multiple (semantic) views and so on.

Projection and/or display surfaces may have associated and/or assignedsemantic identities and/or further inferred semantics.

The system infers the indicated and/or pointed objects/controls based onthe analysis of superposed endpoints and/or obturations by the userhand, arm, finger and/or associated semantic artifacts asdetected/mapped/composed/analyzed by/at/based on the observing entity(observing field of view and/or orientation) within the pointing and/orindication activity and/or semantic time.

The system may perform semantic analysis, composite inferences,(semantic) projections and/or mapping of/between the displays, UPIs,observing semantic artifacts and/or profiles.

The user may use collimation, indication, pointing, orientation and/orlocalization to indicate and/or select artifacts and/or semantic groupsin the environment; further, the user may use various inputs and/ormodalities (e.g. sensor/endpoint movement, gesture, voice etc.) toindicate further semantic identity discrimination and/or actions thatapply to such artifacts and/or semantic groups.

The user may indicate, point, localize, collimate and/or specifysemantic groups of artifacts and/or composable semantic identities.

The user may indicate assembly endpoints and/or areas using collimation,pointing and/or localization techniques.

The system may infer composable semantic identities based on theindication and/or collimation by the user of semantic groups ofartifacts. In some examples, the user indicates by a hand gestureoriented and/or collimated (endpoints) (mapped) towards and furtherdownward direction of a stacked group of speakers indicating that thespeakers need to be shut/turn down/off; thus, the system may infer,challenge and/or acknowledge that the speakers on the left side of thewindow need to be shut/turned down/off.

The composable semantic identities may be used to further specify thelocalization and/or capabilities (e.g. the speakers on the left side ofthe window, the chair by the window, steering (front) wheel, front wheeletc.).

The system may determine and/or collimate on particular semantic unitsassociated with particular semantic identities and/or semantic shapes.In some examples, the system infers and/or is instructed to collimate on“the chair by the window” and based on the circumstance (e.g. user beingimmersed and/or watching a display and/or further sale and/or furnishingsimulation) the system may collimate on the circumstantial object(chair).

The system may collimate cameras and/or sensing entities in theenvironment and based on the collimation orientation, semantic flux,semantic shaping and/or further semantic analysis associates particularsemantic units and/or fluxes with the objects in the environment asdetected by a camera and/or sensing entities. In some examples, thesystem orients the sensing entity (lens/camera/observing field of view)in a retail store and based on sensing, communication, localization,semantic identification and/or semantic shaping determines varioussemantic units, shapes, semantic identities and/or further associations(e.g. between semantic units and/or shapes associated with a component,item, article, module, robotic post carrier—e.g. S3P3—and/or furthercargo). As such, the system overlays and/or renders on the displayinformation associated (e.g. based on an identified, ad-hoc and/oraccessible semantic flux/stream and/or further semantic analysis) withthe identified and/or further collimated objects and/or associatedsemantic units. Further, the system may use collimation and/or overlaymanipulation for further inferences.

In further examples, the user may explain the semantics of particularmovements and/or gestures. Alternatively, or in addition, the system mayuse writing gestures and/or voice to explain another gesture. In anexample, the systems infers by sensing based on a wearable and/oroptical sensor that the user has specified ORDER MY FAVOURITE FOOD FORTHE MOOD and further specified the gesture for such command; it is to beunderstood that between the gesture explanation and/or the shortcutgesture the system may require a particular character (e.g. ‘/’, ‘-’etc.), period of time and/or a sematic time.

The system may select particular artifacts (e.g. on a screen, in theroom etc.), objects, areas and/or endpoints based on the writing and/orother gestures (e.g. the user writes/specifies THE BUTTON ON THE TABLE,THE CHAIR BY THE WINDOW etc.). Alternatively, or in addition, the systemmay use indication and/or collimation techniques to specify, select,manipulate and/or observe particular artifacts (e.g. on a screen, in thestore/room etc.), objects, areas and/or endpoints. The system mayperform tracking of components, modules and/or posts and/or furthercomposes and/or infers semantic groups, semantic identities, semanticbudgets, mission readiness and/or completion. In some examples, thesystem determines and/or pursues composable capabilities, components,modules and/or further posts; further the system routes them from/withinthe supply chain to determined composition/assembly/meeting endpoints.

Once at a composition assembly endpoint (e.g. within a budget and/orsemantic time) the system performs semantic factorizations and/orfurther infers semantic groups of (required/reasonable/likeable etc.)capabilities, semantic identities, modules, parts, posts and/or furtherassembly readiness factorizations. It further notifies (e.g. viaaugmentation, flux/gate) a user/assembler of the assembly readiness(factorization) and/or assembles the components within a budget and/orsemantic time; once composed ready (e.g. as inferred by the systemthrough sensing, fluxes and/or semantic post/module interconnects,challenge-response, semantic factorization, quality, test and/or furtheranalysis) the system may infer the composite semanticidentity/capability. Once a composition semantic identity/capability isinferred the system may determine that the assembledidentities/components/modules/posts are comprised within the compositionsemantic identity/capability. Challenge-response with collaboratorswithin and/or outside a mission may determine further inferences,renaming and/or associations of the compositional semanticidentity/capability. Further, the system may determine the gating formissions, composite semantic identities/capabilities and/or endpointsand/or further publishes, gates and/or budgets the composed capabilitiesand/or semantic identities.

The system may allow/disallow the access, ensemble, positioning,locking, connecting and/or loading to/of components, modules, posts,cargos and/or other artifacts based on semantic analysis includingaccess control.

The semantic components may explain to each other the meaning of variousinputs, outputs, signals, characteristics, movements, localizations,behaviors and/or further challenges. In some examples, the explanationsmay be based on redirecting the learner (by the explainer and/or basedon a explainer challenge) to a repository and/or address (e.g. site,page, channel, account, semantic identity, document, paragraphs etc.)comprising the explanations.

The system may transfer, enable/disable and/or validate/invalidatesemantic network models, semantic artifacts, wallets, authorizations,credentials and/or further profiles to the composed semantic identitiesand/or artifacts. The transfer may happen between the start and end ofan activity if the system requires the (partial) assembly capability inorder to complete the particular and/or related inferred activitiesand/or start further activities. Thus, at any particular (semantic)times only relevant capabilities, are allowed and/or pursued at theassembly and/or assembled. As it can be observed the start/endsuperposition/hysteresis/activity (time) intervals and/or (further)routes may comprise other components/activities start/end,start/completion and/or similar and/or further semantic identities; assuch, the system may transfer, enable/disable and/or validate/invalidateartifacts based on flows of semantic route activities (and/or associatedsemantic times) and/or further sematic analysis.

The system may perform access control on enclosures and/or interconnectscomprising modules. It is to be understood that the access control maybe based on sensing, biometrics and/or further techniques such asexplained and/or cited in this application.

The system may observe the environment, procedures and/or protocols ofthe assembly and infers and/or factorizes a quality indicator/factorbased on whether they may follow procedures, protocols, pose hazardsand/or risks to the assembled artifacts and/or (associated)capabilities. The assembly quality factors may be further used todetermine composed readiness (indicators/factors) and/or insurancepremium factorizations.

The readiness may be factorized based on success, failure and/or othersimilar indicators and/or factors.

The readiness/non-readiness may be further associated via semanticartifacts with enable/disable, activate/cancel, ON/OFF, ALLOW/BLOCK,DO/DON'T, YES/NO, START/END, START/STOP, validate/invalidate,follow/don't follow and/or similar type inferences.

The system may allow the assembly activities to pursue only whenreadiness gating criteria are met and/or readiness factors are higherthan a threshold and/or within an interval; in such cases, the systemmay turn a readiness indicator to on and further allows, notifies,challenges and/or pursues the assembly activity and/or actors. When thereadiness criteria is met/not met the system allows/blocks the assemblyactivity and/or further provides explanations on why the readiness ison/off and/or superposition.

Readiness factors may be based on indicators, factorizations and/orfurther inferences such as likeability, hazards, risks, success, failureand/or similar.

Readiness may be based on starting an activity while having theparticular semantic identities required to complete the activity and/orgoal (e.g. move a car requires to have a key or wallet).

In further examples, the system may activate/deactivate/enable/disablecomponents, modules and/or posts. The system may determine thecomponents, modules and/or posts by location and/or selection (e.g. userpointing, gestures etc.).

The system may induce, determine, publish and/or diffuse(capability/artifact) readiness based on semantic gating and/or furthersemantic artifacts having low drift, shift, orientation and/or entropyfrom the readiness semantics. In some examples, the system publishes(S2P2) (AT) (EP1) READY TO ANALYZE, CRITICIZE AND/OR PRAISE JANE'sHEALTH AFFAIRS ARTICLE ANALYSIS.

In further examples, the system publishes (S2P2) (SU1) (AT) (EP1) FAILED(TO CONNECT) (AND/OR) (TO ENSEMBLE) (TO BE ENSEMBLED), (S3P3)/(S2P2 ANDS2P3) COULD NOT BE ENSEMBLED AT/BY (EP1) (S3P3), EP2 AND EP3 COULD NOTBE ENSEMBLED AT EP1, S3P3 ENSEMBLE(SUCCESS)/(FAILURE)/(BLOCKED)/(BLOCKED BY/AT S2P2)/(NOT ALLOWED)/(NOTALLOWED BY S2P2)/(NOT ALLOWED BY S2P2BUDGET/BEHAVIOR/RULE/ROUTE/CONVICTION/CAPABILITY/READINESS)/(NOT ALLOWEDBY S2P2s LATCH/HOOK), S3P3 WAS SUCCESSFULLY BUILT/ENSEMBLED (FROM S2P2AND S2P3), FAILED TO BUILD S3P3, S3P3 BUILT IS BLOCKED (DUE TO THEABSENCE/CONVICTION/READINESS OF S2P3), S3P3 BUILT IS UN-BLOCKED/READY(DUE TO ARRIVAL) (AND CHALLENGE/PERSUATION) (OF)/(BY) (S2P2) (OF)/(BY)S2P3) etc.

It is to be understood that in the examples throughout this application,the semantic compositions of multiple variants comprised betweenbrackets whether implicit/intrinsic or not are preferably coherentand/or with low confusion factors.

We mentioned that the system may infer elevated confusion circumstancesin relation with goals and/or artifacts. The system may pursue confusionreduction at various semantic times and by various means. In someexamples, the system records the confusion/confused goals, behaviors,route/trails and/or further artifacts with further explanations,renderings and/or related artifacts on what/why/when/how/where theconfusion is/occurred. At a later semantic time the system may revisitsuch confusion inferences and potentially pursues confusion reduction byleveraging newer inferences and/or further related artifacts. In someexamples, the system stores descriptions, renderings, multimedia,sub-models, semantic trails, DNA signatures and/or further artifacts toremember and/or keep track of confusion, explanations and/or relatedartifacts.

The system may infer, determine, publish and/or diffusereadiness/non-readiness, completion/non-completion (e.g. of a goals,route etc.), achievement/non-achievement and/or further similarinferences; in some examples, such activities are associated withsemantic artifacts. It is to be understood that the intrinsic behaviorof semantic artifacts may be also be considered; as such, the system mayor may not publish intrinsic behaviors. Further, the system may publishonly particular polarity and/or entropy (e.g. publishreadiness/completion/achievement but not publish nonreadiness/completion/achievement).

The system may project success, failure and/or further associatedsuperposition intervals based on readiness/non-readiness,completion/non-completion (e.g. of a goals, route etc.),achievement/non-achievement, approval/not approval and/or furthersimilar inferences and/or combination/composition thereof. Further, itmay project what needs to occur and/or to be done to steer and/or orientto success and/or failure within the semantic superposition interval,endpoint, view, flux/network and/or (observing) field/environment.

In some examples, the allow/block, readiness/non-readiness,completion/non-completion, achievement/non-achievement, accepted/notaccepted, approval/not approval and/or further similar inferences and/orartifacts may be based, associated and/or comprise supervisoryactivities, artifacts and/or semantic identities.

The readiness/non-readiness may be inferred based on the composition offurther indicators (e.g. quality, likeability etc.) at (assembly)endpoints and/or further routes.

The likeability indicators may be factorized based on quality indicatorsat assembly endpoints and/or routes. As such, the system mayaffirmatively/non-affirmatively factorize likeability indicatorsassociated with artifacts if the quality indicators associated with theartifacts are affirmatively/non-affirmatively factorized.

The system may also use likeability indicators in order to inferreadiness, project/pursue compositions and/or assembly of artifactsand/or capabilities.

The system uses overshoot and/or undershoot for managing expectations,success and/or failure (e.g. realizations within overshoot and/orundershoot).

As mentioned, the system uses various projections, routes and/or rulesfor generating expectations of realization/non-realization,success/failure and/or further undershoot/overshoot intervals. Duringprojections the system may determine worst case/best case and/orovershoot/undershoot scenarios and thus the expectations, factors,orientations, semantic (superposition/hysteresis/damping) time/indexingand/or further artifacts may be based on slightly and/or lowdrifted/shifted/entropic artifacts comprising the middle of the intervaland/or endpoints of such projections.

The system may publish success and/or failure of goals, readiness,ensemble of semantic identities etc. In further examples, the systempublishes and/or gates the explanations, causes, plans and/or furtherroutes of success and/or failure.

The system may provide and/or be provided with explanations on/ofwhat/why/when/how/where success and/or failure is whether complete orpartial (e.g. within a superposition (time) interval). Thus, based oncircumstances the system may determine whether to strive, allow,diffuse, continue and/or block/wait based on complete or partialrealization. In some examples, the system projects that the risk ofnot-realization is low at particular semantic time (s) and hencepursues, allows, diffuses, unblocks related and/or other inferences byassuming the required, resonant and/or complete realization. If therealization doesn't occur (in a semantic time) the system may furtheruse prior alternate routes/projections, project inferences and comparesassociated artifacts with the projected realization related artifacts.The system may publish blocking/obstructions and/ornon-blocking/promotors of goals, readiness, ensemble of semanticidentities etc.

Challenge-response communication may be augmented with explanations ofwhat/why/when/how/where particular inferences, activities and/orreadiness could or couldn't be completed. The augmentation may comprisefurther explanations on budgets constraints, access and/or furthersemantic times.

The responses to challenges to particular collaborators may compriseunknowns and/or semantic superposition intervals (e.g. a challenge toentity B such as “are you infected” might determine a (superposed)response of “unknown”/“don't know”; further, it may determinesuperposition at B and/or semantic times related with “I am potentiallyinfected, how to find out for sure (e.g. eliminate superposition) and/orachieve non-infection and protection (readiness) using particularactivities”.

The semantic posts and/or (composite) carriers may be used for virtualshopping and/or in virtual (retail) stores. As such, the semantic postsmove and/or roam around a physical and/or virtual store, warehouseand/or another facility and let the user to remotely observe, select,pick, carry and/or pay for goods.

In further examples, the semantic robotic devices may be used in virtualhealthcare and/or hospital environments. In some examples, the roboticdevices augment imaging modalities, surgeries, patients, logisticsand/or other operational needs.

In other examples, the semantic robotic devices may be used in sportingevents such as attending physical and/or virtual sporting events. It isto be understood that potential ticket purchases may be based onsemantic flux bargaining and/or budgeting as explained in thisapplication. Further, the semantic robotic devices may interact and/orcommunicate with coaches, players and/or associated semantic roboticdevices before, during and/or after sporting events.

The semantic robotic devices may interact, communicate, publish, post,gate and/or act on behalf of its temporary/permanent user (s) and/orsemantic groups thereof. Further, they may be given access to its user(s) and/or semantic groups credentials, wallets, accounts, channels,feeds, fluxes, streams potentially in an access controlled and/or gatedmanner.

The semantic posts may be suggested, marketed and/or rented at venuesand the user accesses the storage location and/or enables the devicesbased on access control, credentials, wallet and/or furtherreceived/generated authorizations. In some examples the systemsuggestions/marketing is based on (semantic)targeted/channel/flux/stream/video augmentation, likeability, missionand/or further resonance augmentations; alternatively, or in addition,renting applications and/or flows may be used and/or coupled with thesuggestion/marketing semantic artifacts. Once rented, the system and/oruser may transfer additional semantic artifacts, tickets, access, routesand/or profiles to devices in order to enable those to roam within theallowed facilities and/or behave based on user preferences. A user mayreturn the devices at particular drop-off locations and further ratethem, particular usages, characteristics and/or semantics; furtherexplanations may be provided.

In some examples, the robotic devices are assembled at the(renting/provider) venues based on user/renter preferences and/orrequirements.

The user, owner, supervisor and/or system may guide the behavior ofposts in venues. In some examples, the posts are instructed (e.g. basedon user and/or owner inputs) to perform activities such as to go totheir ticketed seats, leave the venue, come home, go to a storagelocation, return to the permanent owner (e.g. rentingentity/venue/company etc.) and/or other activities. The posts may havehard routes which allow them to challenge, recharge, retire and/or go tostorage when the energy depletion, capabilities and/or furthercircumstances would not allow them to complete an (user/owner related)activity.

The rented semantic posts may infer interests and/or further associatedactivities of renters and/or supervisors based on semantic profilesand/or semantic analysis of messages, posts and/or further challenges;the posts may orient, focus, stream and/or express challenges/opinionsbased on inferred interests. Further, the semantic posts may substituteand/or augment the streamed information (e.g. translate and/or furthersubstitute sounds, moves, play schemes etc.) in order to affirmativelyresonate with the user's interests.

Once the rental (or temporary supervisory operation of a renter) periodand/or activity is over and/or renter instructs the post to return tothe owner and/or storage, the post may erase, invalidate and/or disposeof the personal identifiable information, semantic profiles and/orsemantic artifacts associated with the renter.

Based on owner's preferences the post may preserve the informationlearned during rental period and/or return to a baseline memory and/orsemantic model before the renting activities and/or period. Duringrental the system may use the user's semantic profiles and/or furtherartifacts to perform semantic inferences and potentially store them intoa distinct collaborative model. Once the rental is over, based onpreferences, the post may keep and/or fuse the collaborative model intoits core/base model, publish, expire it, invalidate it and/or dispose ofit. In the case of the publishing the publishedthemes/routes/rules/model/capabilities may comprise the explanationsand/or artifacts learned during rental period and may be furtherpublished on (web/flux/media/user/group/venue) channels; furtherrenters/supervisors/owners may purchase/budget/use such publishing fortheir own inferences, analysis and/or to upload of the models duringtheir operating periods.

The sporting and/or event venues may have particular sections, areasand/or endpoints assigned to semantic posts attendance, themes,socializing and/or storage. Sometimes sections, tickets and/or seats maybe inferred and/or assigned based on semantic inference; further, theymay be subscription based.

Further, the semantic posts may organize in semantic groups based on thecharacteristics of operators and/or supervisors. In some examples,semantic posts associated with operators of age 21+ may have lessrestrictions on alcohol related content, challenges, discussions,postings, marketing and/or other artifacts.

The semantic posts in attendance may point sensing, observe the semanticfield, stream information and/or further provide augmentation and/oropinions to their temporary and/or permanent users, supervisor and/orowners devices and/or further post on (associated/relevant) channelsand/or fluxes. Further, they may be used for crowdsourced sensing invenues.

It is to be understood that the semantic posts may or may not comprisemobility modules. Further, they may comprise multiple modules that canbe rented by separate users and/or supervisors at the same time. In someexamples, posts with multiple camera modules are affixed on an eventvenue structure; the camera modules may be rented and/or manipulated byseparate users. Further, the renting rates may be based on thepositioning and/or further desirability/likeability of the positioning,field of view and/or further semantic field in the venue and/or for anevent.

The start of activities from the semantic view of the post or awell-informed low distorted/drift/entropy augmented party may comprisethe post's/artifact's inference projections and/or furthermeasures/counter-measures in order to achieve the mission; the end of anactivity may represent the realization of the gating criteria and/orfurther success/failure factors. From the semantic view of anill-informed party (e.g. inferring, determining and/or having highdistorted/drift/entropy artifacts) the start and/or end of activitiesmay differ from that of an well-informed party; the ill-informed partiesmay use counter-measures and/or challenges in order to reducedistortion/drift/entropy in rapport with a well-informed party. Theinferences between ill-informed and/or well-informed semantic identitiesand/or indicators may be based in H/ENT inferences.

In some examples, the gating criteria and/or counter measures may bebased on being well informed in regard to published semantics.

The system may infer and/or determine more abstract counter measuressuch as not being predictable in rapport with various semanticidentities including self. In further examples, the system may usecounter measures such as increase/decrease stimulus, distortion,confusion or de-coherency at semantic times.

The system may determine and/or infer effectiveness indicators/factorsin rapport with counter measures. The effectiveness may be based on thesteering of the goal in the desired direction.

A user may transfer credentials, profiles and/or wallets to its semanticrobotic devices using any access, communication and/or storagetechniques explained and/or cited in this application. Further, suchcredentials and/or wallets may expire based on semantic time.

Analogously, the semantic posts and/or (composite) carriers may be usedfor other physical and/or virtual environments (semantic) fusion.

The system implements safety protocols and/or insurance based onsemantic times and/or inference in the semantic field. In some examples,the system infers that an entity is at an increased risk and/orhazardous circumstance (orientation) and thus it suggests and/or remindsof counter measures (e.g. a person glycemia goes high as the personskips the prescribed diabetes medication semantic time, a post goes lowon energy budgets and thus the system may suggest disablement of somenon-critical capabilities etc.).

The system may calculate insurance premiums (for each shipment, orderand/or transaction) based on projected and/or further defined semanticroutes for transportation, storage and/or movement of items and/orfurther cargo and whether those locations have counter-measures againstthreats, hazards, non-affirmative safety/quality/rating/budget/(semantictime) indicators and/or further consequences. It is to be understoodthat non-affirmative safety/quality/rating/(semantic time)/budgetindicators refer to indicators which determine decaying ofsafety/quality/rating/(semantic time)/budget affirmative indicators;thus, by H/ENT, some counter-measures may also determine thefactorizations of affirmative safety/quality/rating/(semantictime)/budget indicators.

The system projects and/or define transportation routes based on reducedhazards, insurance rates and/or providers, semantic time, investmentbudgets; alternatively, or in addition, the system projects and/ordefine transportation routes based on increased safety, quality,ratings, income. It is to be understood that the reduced/increasedindicators may also mean increased/reduced hysteresis (semantic time)associated with the indicators (e.g. reduced hazard means that a slowhysteresis interval and/or “later” type inference towards a hazard arepreferable; analogously, increased safety means that a fast hysteresisinterval and/or “earlier” type inference towards safety are preferable).

The system may further use information about cargo content (e.g.potentially from an invoice, bill of lading, PO etc.) in order todetermine optimal routes and/or further insurance premiums. In furtherexamples, the system detects that a hazardous situation occurs by thedeparture and/or further absence of a supervising semantic identityand/or further counter measures (e.g. user leaves the house for aprojected long time in rapport with a threat such as the gas stove inthe house being open, the user leaves the house for a projected lessthan one hour and the gas stove is open and there is no gasdetection/filtering/dispersion/suction/extinguisher available at theprojected threat/hazardous location in a safety hysteretic semantictime).

A semantic virtual store/facility/environment may comprise renderings,streams and/or fluxes of a physical store/facility/environment (e.g. asobserved by sensing) and/or further renderings, streams and/or fluxes ofvirtual stores/facilities/environments.

A semantic post may comprise a physical post and/or virtual rendering ofthe post. During a shopping session they may substitute one for theother while the system renders the shopping session of the post.

A substitution of the physical to virtual post may happen when thesystem wants to roam from a physical venue to a virtual venue and/orwhen the physical post cannot be present in the desired shoppinglocation at a desired (semantic) time. Nevertheless, the system and/oruser may switch the physical post to virtual post and/or vice-versa atany time based on profiles, preferences, semantic time and/or furthercircumstances.

In some examples, the system recognizes the product in the shelf anddisplays it again in order to be more readable. As such, the system mayperform overlays of the article renderings and/or further informationabout the article, comparisons and/or opinions.

In some examples, the store comprises physical and/or virtual artifacts,rooms, locations, areas and/or renderings. As such, the system may mergesuch artifacts into a coherent view and/or use semantic analysis forlikeable, satisfactory, coherent and/or further customized experiences.

When shopping, the system may instruct the shopping semantic posts toroam to various areas, locations, endpoints, particular articles,categories, semantic groups, routes and/or pick particular items basedon likeability, budget, need and/or further circumstance factorization.While roaming, the semantic posts may present to the shopper the videofeeds and/or further identification of the environment, articles and/orartifacts at particular locations.

The user may instruct the semantic post to socialize and/or furtherpresent opinions to other shoppers (e.g. posts, people, users etc.) onvarious items and/or further environment circumstances (e.g. the colorof the floor, cleanliness, safety etc.). While shopping, the system mayperform comparisons of the articles in the shelves with other similararticles found on other shopping venues (physical and/or virtual).

It is to be understood that the socialization means challenge-responsewith shopping participants, messaging, posting on fluxes/feeds ofparticular participants and/or semantic identities and/or furtheraugmentation.

The system may use shopping lists comprising articles to be purchasedand inferred based on sematic factorization, supply and/or demand and/orfurther semantic analysis.

In further examples, the shopping list comprises wish list items whichthe system identifies based on user input, sensing (e.g. rf/rfid reads,semantic shape, object recognition, scans etc.), likeability, resonanceand/or further semantic analysis. In some examples, the user scans anitem and rates its desirability and/or likeability; based on furtheranalysis, the system may further challenge the user (for feedback)and/or adjusts the desirability and/or likeability factors.

The user/shopper may specify the behavior, semantic routes and/ortrajectory of semantic posts in stores. In some examples the systemspecifies the semantic routes and/or semantic times of posts roaming instores. In further examples, the posts may infer the routes and/ormovement based on circumstances, optimizations, profiles and/or furtherpreferences.

In further examples, the user may use indication, collimation and/ormanipulation techniques to select, render, manipulate, observe and/oranalyze articles and/or further artifacts. The user, system, fluxesand/or post (s) may collaborate and/or decide whether to add articles tothe shopping list, buy them and/or add them to a further likeabilityand/or semantic factorized list.

Further the user may use collimation and/or manipulation techniques tonavigate around various environments and/or issue commands to theenvironment (whether physical and/or virtual). In some examples, thesystem manipulates articles, carts, handles, doors, key fobs, remotes,post's arms, remote/virtual arms, gloves, virtual grippers/hands and/orother manipulation artifacts.

The system may allow the collimation, manipulation, rendering,navigation and/or observation of endpoints, areas and/or furtherartifacts based on semantic analysis including access control.

The system observes semantic units associated with various semanticidentities which are further identified by additional sensing.

The system may use the fusion of physical and/or virtual environments inretail stores, hospitals, entertainment, home, meeting and/or eventrooms/facilities and/or other venues/environments.

In some examples, the system needs to conceal and/or cloak the movementof posts, vehicles and/or devices at particular endpoints and/or fromparticular monitoring entities. As such, the system uses trajectoriesand/or endpoints with low entropic characteristics in rapport with thecharacteristics of the artifacts (e.g. chooses endpoints with darkvegetation and/or nighttime for dark posts, vehicles, objects etc.).

The system may adjust the characteristics, groupings and/or wavelengthsof sensing meshes in order to control the absorption of light and/orfurther electromagnetic spectrum at particular endpoints and/olocations. In some examples, the system has information that particularlocations comprise and/or are monitored by particular wavelengths,capabilities and/or entities and as such it adjusts the absorptionspectrum to counter-measures against those capabilities and/orwavelengths. In further examples, the system superposes and/orconditions signals in such a way that the (back) scattered, reflected,refracted and/or transmitted radiation/wave/sound projects and/orgenerates unmodulated, wideband, non-coherent, confused and/or distortedsignal at the monitoring entities. The system reflects/generates signalswhich blend into environment (e.g. have low entropy, drift and/or shiftfrom environmental circumstances) and further do not allow themonitoring entities to discriminate and/or detect the semantic cloakedobjects in rapport with the environment at particular endpoints and/orlocations.

In further examples, the system uses sensor and/or rendering artifactsto adjust and/or render the appearance of various objects. In someexamples, such sensors and/or renderers may comprise glasses, lenses,appliances, wearables and/or any other sensing and/or display artifactsmentioned in this application.

The semantic cloaks may generate and/or induce (at the monitoringentities) artifacts which are different than the artifacts of thecloaked entities (e.g. induce the shape of a DELOREAN instead of theshape of the actual beetle). In further examples, the sematic virtualcloaks induce different inferences at particular entities, semantictimes and/or profiles (e.g. induces DELOREAN shape and/or inference forJohn and/or its (wearable) observing semantic entities and, inducesbeetle shape and/or inferences for Jane and/or its (wearable) observingsemantic entities). It is to be understood that the system may adjuststhe cloaking and/or rendering at any time, particular semantic timesand/or based on particular user conditions and/or preferences.

The system may use 2D/3D printing for generating/augmenting components,fairings, stickers and/or appliances which may allow and/or be enabledfor cloaking, concealing and/or likeability as per mission goals.

The system may use affirmative/non-affirmative (self) appearancesemantics in order to determine/project consequences and/or furtherfactorizations. In some examples, the system determines that a collisionand/or route with/by a steel spike, post or other object may causeappearance/aesthetic/health damage (e.g. causing decaying of likeabilityand/or un-likeability of self with self or others) such as scratchesand/or arm (joints) twisting and thus, it may further factorize itsinferences as a potential hazard and/or non-likeable leadershipartifact.

Based on health conditions and/or treatments, the system may usesemantic cloaking to generate, induce and/or factorize beneficialinferences and further reduce symptoms and/or hazards for patients.

The system may cloak, simulate and/or augment particular objects andthus implement more effective therapies.

Further, the system may allow the manipulation of artifacts inphysical-virtual environments for more effective procedures, health,emergency and/or communications.

The system may need to (re)distribute traffic so to avoid high entropicadjacent zones and/or allow a more even diffusion in inferred, selectedand/or particular areas; thus, the system establishes, factorizes and/oradvertises incentives, rewards, budgets, indicators and/or further takesmeasures/counter-measures for increased likeability and/or resonance forparticular semantic identities, profiles, objects and/or furtherartifacts which can determine the diffusion and/or movement of suchartifacts to the associated incentivized, advertised, likeable and/orresonant zones.

In further examples, the system projects and/or further takes countermeasures for traffic jams. As such, the system infers that the potentialhysteresis and/or damping based on vehicle movements and/or furtherbraking/acceleration at particular endpoints may create fluency issuessuch as low (affirmative) fluency indicators factorizations (and/or byH/ENT high non-affirmative fluency indicators factorizations). Thus, thesystem may takes counter-measures and/or adjusts the speed, accelerationand/or braking of particular vehicles in order to reduce trafficdamping/hysteresis at (projected) endpoints and/or optimize fluency(e.g. increase affirmative, decrease non-affirmative). It is to beunderstood that the braking/acceleration may be detected based onsensing whether from a sensing infrastructure and/or in a crowdsourcedmanner (e.g. from one vehicle to another).

In further examples, the system is challenged to explain the benefitsand/or drawbacks of (why (SHOULD) DO/(HAVE DONE)/FOLLOW and/or why(SHOULD) NOT DO/(HAVE DONE)/FOLLOW) particular inferences, movements,routes, transitions, resonances, identities, groups and/or furtheractions. It is to be understood that the system may be challenged bysemantic identities and/or further artifacts including self.

The user may indicate how the radiation, ionizations, fumes, gas, waves,charges, sound and/or other sensed phenomena and/or artifacts should orshould not move and/or diffuse within a mapped environment.

The system stores, infers, publishes and/or gates counter measuresand/or mitigation of a (projected) hazard, hostility, offensiveness,risk, situation and/or action based on inference/determination of highentropy consequence semantics in rapport with the (projected) hazard,hostility, offensiveness, risk, situation and/or action (e.g.halocarbons are fire retardants, non-flammable and/or reduce oxygen whensprayed on surfaces and further counteract firehazard/offensiveness/hostility).

The counter-measures may be used to (affirmatively or non-affirmatively)index and/or factorize a stimulus. In some examples, the stimulus may beactivity stimulus, achievement stimulus, sensitivity stimulus,sensory/sensing stimulus, budget stimulus, economic stimulus and/orsimilar.

The counter-measures/mitigation may comprise own, environment, and/orcollaborators (semantic groups) capabilities and/or behaviors. Thecounter-measures and/or mitigations may be circumstantial,collaborator/flux/group, location and/or endpoint based.

The system may use semantic resonance for inferring and/or applyingcounter-measures.

The counter measures help keep and/or steer inferences withinrequired/specified behavior, orientation, drift, shift, semantic timeand/or entropy when high drift, shift and/or distortion occurs.

In some examples, “keep the area safe” goal is distorted and/or highdrifted by the hostility of an intruding drone and hence the systemapplies counter measures to keep it safe and reorient/steer/return it tothe intrinsic and/or desired behavior. Further, the system may usesemantic artifacts of counter measures within semantic times (e.g. warnthe drone, applies more drastic measures if the warn didn't work/steeras expected and/or drone is still hostile etc.). It is to be observedthat the counter measures may be factorized based on consequenceindicators/factorizations and/or further (associated)indicators/factorizations.

In other examples, the unprojected energy budget depletion and/ororientation require counter measures such as reduction of energyconsumption and/or disablement of some capabilities for a semantic timein order to restore energy budget depletion orientation.

In further examples, the system identifies semantic identitiescomprising counter measures. In some examples, during a virus spreadprojection the system infers identities such as “JOHN and friend wearmasks in and around the hospital”, “JANE wears N95 mask”, “S3P3 operatorwears no mask”, “S3P3 has not been disinfected after being used” whichfurther comprise counter-measures (e.g. wearing mask and/or further moredrastic/strongly factorized mitigations such as wearing a N95 mask whichis better rated for hazard medium/air/drops filtering). It is to beobserved, that the hazard at particular locations and/or endpoints isinferred based on hazardous and/or non-hazardous interactions,environment, factorizations and/or further mitigation by countermeasures. Further, the particular locations visited by potential hazards(e.g. virus bearer (S3P3) operator and/or further potential bearerS3P3—if S3P3 and its operator were in close proximity in a semantichazardous time) may be factorized to reflect hazards and/or diffusions.It is to be observed that the S3P3 may not pose highly factorizedhazards if the transmission of the virus and/or hazard diffusion hardlytake place through its particular surfaces, actions and/or (further)interactions; further, the potential diffusion, transmission and/orhazard posed by S3P3 may potentially follow and/or be based on adecaying hysteretic semantic interval which may further depend on theenvironment (e.g. indoor/outdoor, ventilated and/or not ventilatedetc.). S3P3 may be remotely operated and thus the hazardous interactionswith a potential bearer operator could be hardly and/or not diffusibleat all. Other counter measures and/or circumstances may decrease thestress and/or anxiety caused by hazards and/or consequences of infectionat hazard prone (diffusible/diffusable) endpoints (e.g. having(diffusible/diffusable) medication, ventilation to disperse inparticular non-hazardous directions, counter measures or mitigationsagainst the effects of the virus) and/or further available withinsemantic times (e.g. to counteract the infection hysteresis and/orreverse it; damp the infection diffusion etc.).

The system may use sensing, actuation and/or further counter measures todisperse and/or reduce the hazardous circumstances at locations and/orpotentially direct/route them (e.g. ventilate and/or disperse fumes toparticular non-affecting directions, toward a window etc.).

The counter measure inferences may be used to control, steer and/orreverse trends (at endpoints) as determined by statistical models andinferences.

The system rates and/or insures various artifacts, items, articles,posts, transports and/or cargo based on the risk, hazards and/or furthersemantic indicators/factors posed by various routes and/orcircumstances.

The system may adjust the ratings and/or premiums based on the routecounter-measures which determine thedecaying/factorization/hysteresis/damping of risks, hazards and/orfurther indicators. The risks, hazards and further factorizations may beinferred based on the hysteresis and/or damping of the countermeasuresand their effects in rapport with the insured artifacts. In someexamples, the system determines a risk/hazard/likeability related withlow/high temperature items/areas, associated heater/refrigerant/A/Cunits and/or (associated) semantic posts; if further countermeasures(e.g. heater/A/C capable posts) are available for diffusion, actionand/or readiness within (or to counteract) a hysteretic(non-hazardous/hazardous) (semantic) time then the likeability may befactorized and/or risk, hazard and/or premiums may be further reduced.It is to be understood that the system may consider the hysteresis,damping and/or diffusion on a composite basis (e.g. the discharge,depreciation and/or hazard of a battery pack at (low) (semantic time)temperatures may comprise a hysteresis interval which further may becomposed with the (composite) hysteresis provided by counter-measures ofheater/heating based diffusions, hysteresis and/or posts).

The system may factorize risks, hazards, likeability, indicators and/orfurther premiums based on (composite) semantic indexing, semantic times,hysteresis and/or damping of circumstances and/or counter measures.

The system may determine and/or infer factorization of counter-measures(e.g. likeability, hazard etc.) in particular circumstances.

The counter measures may comprise capabilities for disablement ofartifacts/identities and/or, reduction, counter bias and/or decaying ofgoal/orientation drift/shift/entropy.

The system interprets the risks, hazards and/or further factors in acomposable, hierarchical and/or diffusive manner—e.g. risk of loss ofcapability (including counter-measures) and/or semantic identities (e.g.posts, modules) is inferred based on the risk of loss of (critical)components (e.g. memory module, heater/AC).

The loss of capabilities and/or semantic identities can be temporaryand/or permanent—e.g. risk of loss of post or memory can be temporaryand/or (further) permanent—power goes down and/or memory breaks down.

In case of the loss of capabilities and/or semantic identities thesystem writes down, writes off, disables and/or invalidates from thebooks, contracts, supply chains and/or clause the associated assetsand/or capabilities permanently and/or temporary (e.g. based on asemantic time). In further examples, the system records explanations ofthe circumstances of loss, disablement, write downs and/or write-offs.It is to be understood that the write-downs and/or write-offs may besemantic time dependent.

The system may infer goodwill indicators and/or factors based oninsurance type factorizations and/or further analysis. The goodwill mayincrease as the insurance risk/hazard is reduced and/or likeabilityfactorized. Further, the goodwill may be inferred based on thenegotiations in the semantic network between brokers, insurances, buyersand/or sellers. We expressed that the budgets may be based on a varietyof indicators which may be exchangeable for one another. In someexamples, the budgets may be based on health, well-being, excitementindicators. Further, the system may establish budget indicators and/orfurther leadership based on semantic profiles and/or further inferences(e.g. infers that in order to factorize well-being the leadership budgetindicators may need to be health, excitement etc.). It is to be observedthat while health may be a budget indicator it may depend on otherbudgets (e.g. energy quanta, health service/insurance quanta/premiumsetc.).

Readiness may be based on starting an activity while having theparticular semantic identities required to complete the activity and/orgoal (e.g. move a car requires having a key, credential or wallet, goingto a picnic in a hot day requires water or hydration based oncircumstances). In further examples, such readiness is the gatingcriteria for being allowed out of an endpoint without consequences,being challenged (e.g. you forgot your key, get your key etc.) and/orblocked. It is to be observed that in some examples the semanticartifacts and/or identities are localized by the system at the endpointand/or different endpoints and/or further determine whether they are inthe possession of the bearer while being gated through the endpoint.

In some examples, the gating criteria, readiness and/or counter measuresmay be based on being well informed in regard to published and/orhazardous semantics and/or further artifacts.

We expressed that the system may infer distraction factors associatedwith challenges, activities and/or other artifacts which may determinedecaying of focus, attention and/or budgets in relation with particular(critical) routes, goals, scenes and/or views. Further, the distractionfactors are high when they decay the resonance of the particularresonant goals and/or budgets.

The distraction factor may be used to infer and/or apply countermeasures and/or decay dissatisfaction, concern and/or stress factorsand/or further as a counter measure to uncertainty and/or unknowns. Insome examples, the system uses projected distraction factors in order toinduce resonant superposition (e.g. via challenges, semantic spread,goal shift etc.) in regards with uncertainty, unknowns and/or furtherartifacts generating dissatisfaction, concern and/or stress factors.

We mentioned that the system may fusion the semantic attributes inferredin regard to various user interface controls. In an example the systeminfers that a text box labeled for comments (or similar) and/or havingCOMMENTS leadership is NOT EMPTY. Further, the system expires thecontent of the text box after a sematic time and thus inferring that thetext box is empty. The system may compose the attributes of the windowand the emptiness status (e.g. COMMENTS WINDOW, NOT EMPTY) and thustaking appropriate actions (e.g. notify the supervisor, unmutemicrophone etc.).

The system may use ad-hoc semantic coupling to connect (affirmativeresonant) leadership at endpoints at (resonant) semantic times.

Users, supervisors and/or leaders of views may be coupled withcollaborators via ad-hoc semantic coupling. In some examples, thesemantic coupling comprises semantic analysis inferring collaboratorsinterests, challenges and/or further semantic attributes. It is to beunderstood that the ad-hoc semantic coupling may thus comprise theoptimal user interface controls and/or devices to connect with theoptimal user/supervisor and/or leader which affirmatively resonatesand/or is well-informed on the collaborator's interests and/or furtherchallenges.

Sensing may be oriented for further optimal stimulation from affirmativeresonant leaders and/or during ad-hoc semantic coupling.

The system may identify leadership of distortion, distraction,stimulation and/or further activities and/or counter measures.

The system may gate the artifacts which may generate bad publicity,distraction, distortion and/or ill-inform for the observed semanticidentity (e.g. filter video or sound artifacts and/or signals which maybe non-affirmative resonant at the observed semantic identity in rapportwith projected inferences in the collaborators and/or audience in avirtual conference). As such, the system may project bad publicity,non-resonant projections, distraction, distortion and/or ill-inform atthe semantic identity, collaborators and/or any combination thereof andthus gates such artifacts. It is to be understood that such projectionsand/or gating may be based on the semantic identity, semantic groupprofile and/or theme of the semantic flux conversation.

We expressed that the system may determine appearance/aesthetic/healthdamage based on particular locations. It is to be observed that thesystem may fusion many semantic (theme) perspective views/artifacts(e.g. appearance, health, publicity etc.) when determining thetrajectories, behaviors and/or goals. In some examples, “arm twisting”may signify appearance (not looking good, non-affirmative publicity),distraction, distortion and/or health (functionality). Further, thesystem may determine the impact factorizations and further projectionsin rapport with achieving the goals (e.g. arm twisting may pose healthissues, high costs, risks and/or impairments in the realization of goals(e.g. moving the tea pot)). In further examples, the system may notinterpret arm touching as a hostile arm twisting attempt but instead asa gesture of arm reaching with no hostile intent.

The semantic grids may be associated with monitoring grids attached topower delivery (networks). In some examples, the system uses semantictime management, stimulation, satisfaction/dissatisfaction and/orsimilar to adjust, control, tunnel, diffuse and/or gate consumptionand/or demand. As such, the system projects undershoot of demand,overshoot of capacity, under-stimulation, hazardous shift orientation ofcapacity/demand and/or budgets (e.g. due to budgets required byprojected activities at the semantic time etc.) and hence itenables/disables, encourages/discourages (e.g. by challenges etc.)and/or stimulate/under-stimulate activities in order to preserve anoptimal, likeable and/or desirable capacity/demand (superposition).

In further examples, the system projects and/or determines hazardousconditions at locations and/or endpoints encompassing utility grids.

Analogously, the system may project hazardous conditions in inventories.The system may use sensing techniques to determine inventories, theirlocations, semantic identities and/or further circumstances.

In some examples, the system creates plans which optimizes the executionof activities and/or goals based on semantic budgets and/orcapacity/demand inferences. Capacity/demand and similar are associatedwith conjugate semantics, H/ENT inferences in rapport with one another(capacity vs demand).

We mentioned that the system infers distortion in various situationsfrom various fluxes. Further, distortion may be inferred when a party orartifact use various anchor points to present its knowledge, successesand/or achievements (thus projecting to induce at self and/orcollaborators overestimation of achievements and/or available budgets,and/or underestimation of required budgets and/or potential failures).In some examples, such distortion is based on charts/graphs where thesystem choses anchor points in order to increase and/or decrease therelative distance and/orientation between (the top of) (similar semanticattribute endpoints in) charts/graphs. Analogously, overestimationand/or underestimation is used to downplay failures.

Collaborators may use narratives, options and/or artifacts in order todetermine the system to adjusts anchors. When the collaborator goals areassociated with distortion the system may infer foe and/ornon-resonance. Further, the system may want to damp the resonance withinthe distorted anchor point.

The system may factorize deception based on manipulation of anchorsand/or further distortion.

In some cases such distortion is hostile or ill-intended when the systemknows that the distortion causes non achievement of goals and/or failureor the distorted party.

Foes and/or further distorted and/or overestimating affirmativeinformation in the flux network may induce anchor distortion in order todetermine the system to overspend. The system may counter bias suchdistortion by using defensive behaviors in regard to distorted anchorleadership semantics. Analogously, distorted underestimated affirmativeinformation may determine the system to underspend and thus the systemmay counter bias by using offensive behaviors in regard to the distortedanchor leadership semantics. Analogously, by H/ENT, distortedunderestimated non-affirmative information may be used for deception andthe system may use counter biases to counter act/measure those.

In other examples, the distortion may be used in order to downplaypotential projected non-likeable inferences and/or artifacts. In someexamples, such distortion may be ill, foe and/or hostile projectedand/or well and/or friendly intended. The distortion may causeactivities which are non-affirmative towards the targeted semanticidentity goals.

The semantic posts may infer, localize, manipulate counter measuresand/or perform activities which enable/disable them. Further, the systemmay combine and/or compose such capabilities for creating more effectivecounter measures. As such, the system localizes fire hydrants, airblowers and/or other artifacts; in case of an actual or potentialhazardous condition (e.g. fire due to a highly hazardous gas leak,ignition potential etc.) the system may infer counter measures providedby fire hydrants, air blowers and/or further likeable and/or resonantartifacts. The system may infer potential directions and/or flows ofhazards/egress and further redirect them based on projections ofcapabilities, counter measures and/or tools at the location endpoint(e.g. localizes a window which can be potentially opened and/or broken(by his activities and/or its collaborators)); thus, it (re)directs thehazards and/or egress towards less/non-hazardous and/or affirmativefactorized locations gated by the window. It is to be observed that thehazards may be factorized in regards with particular semantic identities(e.g. gas, fire and/or smoke is hazardous for a person but not thathazardous for a ceramic plate). Further, the window may gate onlyparticular hazards (e.g. it can block and/or hardly diffuse gas but itcannot block light etc.). It is to be observed that the systemdetermines that particular artifacts (e.g. windows) have flow evacuationand/or diffusion capabilities.

The system may further analyze the diffusion of hazards and/or egressbased on capacity and/or budget of egress. In some examples, the systeminfers the capacity and/or budget of egress based on the egress surface(e.g. open/broken etc.) on the window, fitting of the hazards (e.g. gas,people, posts etc.) and/or further flow analysis. As such, the systemmay factorize the egress capacity indicator and/or further semantictimes into further semantic analysis.

Flows may be associated with demand, consumption, traffic, window,ingress, egress, cash, offense, defense etc. In some examples the systemA at a particular endpoint/s has incoming flows and thus increasedingress capacity (e.g. if the incoming flows are particular products),ingress demand (e.g. if the incoming flows are particular consumers)and/or ingress consumption (e.g. if the demand is consumed at theparticular endpoint/s). Analogously, by H/ENT, the system may manifestegress capacity, demand and/or consumption towards other endpoints.

The semantic posts may manipulate tools providing counter measurecapabilities at particular locations and/or endpoints; further, suchtools, their capabilities and/or further sensors/actuators may becontrolled by the semantic posts (e.g. liquid/gas flow rate etc.).

The system controls damping/hysteresis of hazardous circumstances basedon projected incoming and/or outgoing flows/diffusion at the hazardousendpoints.

Damping on mobility, manipulation, counter-measures and/orinterconnection artifacts (e.g. wheels, grippers, lock, latches, links,hydrants, modules etc.) may be adjusted.

Further, the system may adjust the damping of the lockable and/or hookcomponents. In some examples, the system adjusts the preload and/ordamping of (suspension) components attached to band hooks, grippers,arms and/or clips in order to allow the bands to move, extend, support,tension and/or damp artifacts touching them (e.g. a person is holdingand/or is supported by the band and/or post and thus the system adjuststhe tension and/or further damping of the band end hooks in order tooptimally support the person, a person is about to fall on a bandbetween two posts and thus the system adjusts the damping to alleviatethe effects of the fall on the person etc.).

Input/compression damping may be associated with incomingflows/diffusion and/or inferences while the return/rebound damping maybe associated with outgoing flows/diffusion and/or inferences. It is tobe understood that the input/compression damping at an endpoint maycomprise a return/rebound damping from another endpoint as the twoendpoints may be interconnected through oriented links and the dampingpropagates between endpoints between a first and a second (semantic)time.

The sematic post may elevate/ascend and/or descend in order to grip thecounter measure tools, open flows (e.g. allow/open/break window) andfurther orient them to optimal orientations, areas and/or endpoints. Infurther examples, in order to extinguish, route and/or disperse hazardsthe counter measures may be oriented optimally based onovershoot/undershoot inferences; in some examples, they are orientedtowards the middle and/or bisector of the minimum area and/or angledetermined by undershoot and/or overshoot inferences.

The system may infer and/or expire sematic identities based onpossession and/or composition. As such, the system infers “the nursewith newspaper” at a first time but later infers “the nurse withoutnewspaper”; however, based on circumstances, the system may retain thatnurse Jane is “the nurse with newspaper” due to high factorization withself and/or in the flux network. Similarly, the system may assignsemantic identities to endpoints and/or further artifacts (e.g. “theroom with sprinkler”, “the room without sprinkler” etc.). It is to beobserved that the semantic identities may be based on semantic times,attributes and/or (direct and/or counter) capabilities at the endpointsand/or locations.

In further examples, the system infers the presence ofobjects/items/people/posts/(flux owner)/supervisor and/or particular(semantic identities) which observe the semantic field. Thus, the systemmay infer and/or determine witnesses and/or observers of particularhappenings in the sematic field. The system may infer such witnesses byinferring that the particular identified artifacts were observing and/orinferring happenings in the semantic field (e.g. via inferences and/orchallenges from and/or to the witness—I like how John pitched that ball,did you see what John did? etc.).

The system may be challenged and/or redirect questions which doesn'tknow the answers or is confused about. The system may divest particularchallenges for particular themes to various collaborators. In someexamples, John may divest car appearance related challenges to Jane.

The system may broadcast information to devices at and endpoint/locationfor semantic coupling.

We mentioned that the system may infer readiness based on projections ofrequired capabilities, artifacts and/or activities when leavinglocations which comprise (e.g. based on localization) such capabilities,artifacts and/or activities.

In some examples, the system projects (e.g. based on (semantic)(calendar) time entries comprising scheduled shopping) a further(car/truck/post/carrier) transportation activity and thus the need tostart-up the transportation activity and/or artifacts(car/truck/post/carrier) using assigned/associated keys/credentialsand/or devices; localization and/or gating criteria is used forallowing/disallowing a semantic identity for leaving an endpoint (e.g. ahouse, venue, facility, car, carrier etc.) which comprise particular(car/truck/post/carrier) keys/credentials, fobs and/or devices.

Localization techniques has been explained in this application and/orcited applications, the contents of each of which is incorporated byreference.

The start-up of an activity includes starting up an activity withinand/or together with an artifact (car/truck/post/carrier) which providesthe required transition and/or mobility capabilities based on budgets.The system uses localization of credentials and/or further associatedartifacts (e.g. devices, wearables, supervisor, leader etc.) in order toinfer the readiness of pursuing transitions and/or activities in thesematic networks. As such, the system may need the required credentialsto transition, move, start activities and/or pursue goals in thesemantic network model.

Readiness may be the gating criteria for activities and/or furthertransitions between endpoints in the semantic network model. Readinessinference may comprise credentials, identification, wallets, keys, fobsand/or other semantic identities which allow the pursuance ofactivities.

The system may apply semantic profiles of an artifact once is identifiedand/or localized to an endpoint.

In some examples, the system allows items and/or articles in a virtualstore to be published, sold, appraised and/or rated only when the gatingand/or readiness is met. In further examples, the readiness is based ona release semantic identity of a software/hardware application, itemand/or article in a virtual store and the general public may not beallowed to post ratings and/or use feedback controls unless they are inthe targeted release semantic identity (e.g. “released”, “to John”;“released “to public””, “released for comments”, “released for commentsby voice” etc.). It is to be observed that the system mayactivate/deactivate the ratings, comments and/or voice controls and/ordevices based on detected semantic identities which are at the activityendpoint and/or further (attempts) to perform the activity (e.g. allowcomments, allows comment from John, allows comments only if the allowedcommenter has voice processing capabilities, allows comments only fromJohn and/or by voice etc.).

In similar examples with the virtual store the system may allowfeedback, challenges and/or augmentation from the user in variouscircumstances, augmentation interfaces, and/or embodiments some whichare presented in this application.

The system infers required transitions and/or diffusions of credentialsat semantic times potentially as grouped with a/an (activity) user,owner, supervisory, container and/or further artifacts. In someexamples, the system requires transitions, diffusion and/or furtherpresence of a key, wallet, device and/or credential within a containerand/or (mapped) endpoint in order to start a container/endpoint activityand/or transition (e.g. start and/or move a vehicle); as such, thevehicle becomes a highly factorized container and/or further (mapped)endpoint for the key during particular circumstances (e.g. such as carengine started).

Credentials may be associated and/or in possession of a supervisoryand/or leadership entity, activity and/or artifact. As such, activitiesand/or artifacts may require (circumstantial) supervisory and/orleadership credentials to pursue the access to an endpoint, inferenceand/or goals.

The system may infer a required transition and/or diffusion of asemantic group.

The system may not allow the access, transition and/or diffusion to anendpoint if the circumstantial (supervisory and/or leadership)credentials are not within the semantic group, at the same locationand/or in the possession of the transitioning artifacts and/or activity.

We mentioned that the system may replace, substitute and/or induce oneitem/artifact and/or group thereof with another item/artifact and/orgroup thereof at semantic times. (e.g. show John driving a beetle likecar (instead of a DeLorean) after he finishes talking on the phoneand/or is ready to go meet Jane). It is to be understood that the“beetle like car” may be based on likeability and/or affirmativeresonant projections at Jane, John, semantic group thereof and/orfurther circumstances at the activity endpoints (Jane see Johnendpoints) in regards to leadership such as “beetle” (“shape”), “car”(“shape”) (.“shape”) and/or by using semantic profiles.

The system projects (desirable/likeable/required) localization ofartifacts and/or items based on projected and/or ongoing activities atthe locations and further challenges and/or augments the user. In someexamples, “John sees Jane” activity requires John to drive a particularcar (e.g. beetle like and/or capable) towards Jane (projected) endpointand hence the system infers that the localization, orientation,trajectory and/or drift of John and a beetle capable car key/credentialat different times within the activity to see Jane is not normal (e.g.John left/forgot the beetle key/credential on the fireplace). It is tobe observed that the system factorizes a “forget” indicator based onwhether the (projected) distance, orientation and/or drift between themain activity holder/supervisor (e.g. John, John and Jane group) and the(transport/transitioning) (start) (pre-condition)capability/artifact/activity (e.g. key/credential, presence and/orcollapse of key/credential at the (transport)capability/artifacts/activity associated endpoint) and/or further(pre)conditioning budget increases potentially within the goal activitysemantic time (John sees Jane). Additionally, the system may usechallenges, mitigations and/or counter-measures (e.g. such asremind/challenge the user and/or instruct S2P2 to pick-up/grip/use thekey/credential and start the artifact/activity while John isidentified/determined as ready/like/desiring/wanting/instructing tostart the car).

The system may use forget indicators to infer decays of semanticartifacts and/or further semantic routes (e.g. the system infers theroutes and/or artifacts determining forgetful behavior). In someexamples, the system uses forgetful behavior and/or routes to forgetpast experiences, semantic trails and/or artifacts.

We described that the system may infer meal projections and/or furtherpreferences. Such inferences may be based on budgets associated withmeal items, capabilities and/or components such as calories, proteins,carbohydrates etc.

We mentioned that the system may determine leisure (e.g. vacation, mealsetc.), budget and/or energy goals. In further examples, the system usessemantic time management based on projected availability at a particular(projected) endpoints and/or locations. In some examples, the system mayuse “next meal with meat” inference to order, (re)stock, (re)supplyand/or charge from providers the (likeable and/or preferred) meat;however, if the meat cannot be delivered and/or made available at theprojected user's meal location, then the system may either suggest theuser with a new meal location, adjust the meal schedule and/or semantictimes and/or further challenges and/or augments the user.

The system projects likeability and/or further factors to determinetransitioning endpoints and/or routes. In some examples, those are basedon projected needs and/or preferences (e.g. next meal with meat).However, as the overall affirmative (resonant) factorizations are lowand/or decay the system may challenge itself whether the goals can beadjusted (e.g. do I need meat next meal?, why do I need meat? forenergy, protein and/or taste (?), how much energy (budgets) do I needfor reaching the (sub)goal?, can I substitute with protein bars? etc.).It is to be observed that the system may consider weight goal (artifact)leadership semantic attributes (e.g. protein content, taste etc.) andaffirmatively factorize items having leadership in regards to suchsemantic attributes (e.g. protein bar has high protein content). It isto be observed that the system infers high protein content for thesemantic identity of “protein bars” by using semantic analysis on thesemantic identity itself which comprises and/or is composed of“protein”, “bar”.

It is to be understood that the availability of particular artifacts(e.g. meals) at locations may be based on ordering from particularproviders and/or fluxes and/or further delivery (e.g. by semantic postscarriers) at the locations.

The system indexes the significance of the inference based on semanticanalysis inferences. Thus, the system may have a current factorized goalof “50% meat next meal” however, since it cannot have meat next meal itfurther factorizes/indexes the importance (e.g. “60% meat next meal”).The “meat next meal” may be a subgoal of a more strategic goal (e.g.“meat in the next ten meals”, “meat as the protein intake goes/is low”etc.). It is to be observed that the system may use other alimentationitems, inputs, components, artifacts and/or elements (e.g. previousprotein intake inferences) in order to index the significance ofprojected meals and/or further suggestions, augmentation and routing.

The system determines goals, sub-goals, activities and further (sematic)(time) budgets. As the budgets decay the significance and/orsignificance of the goals, sub-goals and/or activities is factorized. Insome examples, the system non-affirmatively factorizes the realizationof goals such as protein intake and/or energy if the sub-goals and/oractivities (e.g. “meat next meal”) (orientation) (is) decays (/decayed)and/or is non-affirmatively factorized/oriented.

Foe generated (counter)measures may generate distortions and/or impedethe signals to be further conditioned, diffused and/or collapsed as pergoals.

When distorted, the system strives to affirmatively factorize (e.g.decay as per goal) distortion by increasing the semantic spread and/orfurther non-affirmatively factorize, invalidate and/or expire thesemantic artifacts and/or leaderships generating the distortion.Further, the system may record and/or learn the artifacts whichgenerated the distortion and may factorize them as foes.

The system may project the offensive and/or defensive activities offriends and/or foes in the best case and/or worst-case scenarios. Assuch, the system may further adjust the friend/foe factors based on theorientation, drift and/or shift between the projections and/or theactuals.

The system may determine leakage and/or damped diffusion orientation atendpoints based on measurement of the first sensor/flux at the firsttime and the second sensor/flux at the second time wherein there is anopen and/or diffusible link between endpoints. In some examples, thelink is associated with a fluid and/or gas pipe and/or diffusionenvironment (e.g. convections, routing and/or dispersion in accesscontrolled areas); in other examples, the link is associated with acommunication (e.g. wired, wireless, media, news, messaging, voice etc.)channel. As such the system determines the orientation of the condition,leakage and/or phenomena (e.g. loss of pressure in pipes, gas leakage;current and/or magnetic flux leakage, information leakage etc.) based onmeasurement from the first sensor/flux and/or the second sensor/fluxand/or further link conditions and/or semantic attributes.

In some examples, the leakage and/or damped diffusion may be associatedwith a (semantic wave) and/or signal.

When performing inferences the system may challenge self and/or semanticfluxes connected to self.

The system uses augmentation constructs and/or artifacts which reflectvarious semantic view insights. In previous examples, we mentioned thatthe system uses more doubtful/uncertain constructs and/or opinions (e.g.I THINK, WE THINK etc.) to reflect (mainly) its insight semantic viewand/or maybe coupled and/or diffused with other resonant (collaborators)insights while allowing other semantic views and/or (associated) fluxesto comprise, diffuse, challenge and/or express doubts and/or driftedinsights in regards to such opinions and thus increasing superpositionand/or semantic spread. In cases when the system does not want and/orneed feedback, increased superposition and/or diffusion in particularcircumstances, locations and/or endpoints then it may skip suchconstructs altogether.

The system may detect deception indicators and/or factors by inferencesrelated with deliberate distortion (e.g. being related with a distortionactivity and/or similar and/or having a goal/mission associated with thedistortion activity) of artifacts.

The system may use mitigations and/or counter-measures to affirmativelyproject and/or factorize (e.g. decay as per goals in order to decreasehazards, decrease non-affirmative resonance) hazardous and/ornon-affirmatively resonant consequences.

The system factorizes non-affirmative consequences at endpoints. Furtherand/or similarly, when making projections and/or endpoint selection inregards with mitigations and/or counter measures the system may considerdiscriminations based on sensing and/or semantic rules (e.g. based onthe number/characteristics of artifacts and/or semantic groups,projection of endurance/survival (hysteresis) etc.).

The system may factorize risk based on and/or for capabilities, budgetsand/or value exposed to hazards. Further, the system may factorize avulnerability indicator for assets based on the risk and/or furthercounter measures available to such assets in respect to decaying risks,expiring/invalidating threats/hazards and/or preserving capabilities,budgets and/or values.

The system uses counter-measures for dispersing hazards and/or threatsto particular orientations, directions and/or endpoints. Further, thesystem directs hazards and/or threats to endpoints and/or locationshaving further counter measures capabilities for further damping,orientation, dispersion, invalidation and/or expiration of threatsand/or hazards. In further examples, the dispersion of hazards requiresthe actuation of access and/or diffusion capabilities (on particularlinks) (e.g. open doors, windows etc.).

The system may know that a gating/access point artifact (e.g. window,door, lock, sink, coupler etc.) may allow the diffusion of particularelements which may further factorize conditions at the diffusedlocations (e.g. increase and/or decrease hazards, allow oxygen in whichfactorizes fire hazards, allows smoke out, allow escape, allow intrudersetc.). As such, the system optimizes the actuation of the gating inorder to achieve goals (e.g. evacuate as many people as possible, saveJane and maybe John, save the moving beetle with Jane otherwise burn itbecause it blocks egress etc.).

It is to be observed that the system may disperse hazards and/orcounter-measures from endpoint A towards endpoint B and/or (further)from endpoint B towards endpoint C and/or potentially towards endpointA. In some examples, the dispersing capabilities at endpoint A arecomposed and/or coupled with the counter-measure capabilities atendpoint B in order to decay, affirmatively factorize and/or damp thehazardous effects (e.g. the hazard at endpoint A is dispersed towardsthe counter-measure (endpoint) field from endpoint B). In furtherexamples, the dispersing capabilities at endpoint A are coupled withdispersing capabilities at endpoint B to determine a compositetrajectory and/or orientation of the dispersion hazardous field (e.g.the system infers dispersing routes). In further examples, thecounter-measures (dispersion) fields, shapes and/or endpoints diffusewith the hazardous dispersion fields, shapes and/or endpoints andfurther neutralize/decay, affirmatively factorize and/or damp it. It isto be observed that the dispersion fields may be inferred and/orprojected based on semantic shaping and/or further diffusion; further,the diffusion may be based on attributes associated with dispersionsspeed, dispersion mass/density, chemical reactions/diffusion and/orother phenomena occurring between the dispersion masses and/or theirinteractions.

The system may project environmental conditions and/or furthercircumstances (at the molecular level, endpoint and further) byinferring and/or applying (learned) semantic resonance between elementsforming covalent and/or ionic bonds.

The system may analyze hazardous circumstances and/or furtherconsequences at endpoints based on the (projected) presence of artifactsat endpoints and/or further based on their profiles, circumstancesand/or further consequences. The system composes and/or analyzes theworst-case and the best-case scenarios between undershoot and overshootand/or optimal/average limits/endpoints.

The artifacts at particular endpoints may cause and/or be affected byhazardous circumstances, dispersion fields and/or diffusion. As such,the system may infer and/or factorize a causal indicator which indicateswhether the artifact was the cause or has been affected by hazard. Insome examples, John drives the car in a hostile and/or hazardous manner(determining hazardous endpoints and/or consequences for otherparticipants) and (by projection) interacts and/or collides with S3P3and further, twisting S3P3s arm; as such, the system factorizes thecausal indicator of collision as being highly (positively) factorizedfor John, its car and/or further group thereof. In other examples, Johndrive his car and S3P3 is distracted by S2P2 and/or is under pressureand doesn't signal in time a hazardous endpoint and/or lane conditionand/or clear a hazard and thus causing John to enter in hazardous areasand/or activities and/or potentially hitting the hazard and/or twistingS3P3s arm in the process; as such, the system factorizes the causalindicator of collision as being highly (positively) factorized for S3P3,its supervisory and/or S2P2 (as supervisory) and/or further groupthereof. The system may divest responsibilities for endpoints (e.g. toJohn, S3P4 etc.) and/or further signal/marks the unavailability (ofS3P3, supervisory etc.) and/or signal/marks hazards/unknows at monitoredlocations in cases of supervisory distraction, low budgets and/or underpressure. It is to be understood that the system (by H/ENT) mayfactorize, invalidate and/or decay availability based on unavailabilityand/or vice-versa. Similarly, the system may factorize hazard/safe,known/unknown and/or other conditions.

Analogously with inferences in regards to artifacts (e.g. windows,container/contained endpoints etc.) having (hazard) flow capabilitiesthe system may perform flow analysis for demand, consumption, traffic,currency, cash, securities, denominators, plays, offense, defense and/orother artifacts and/or semantic groups thereof.

The system may associate offense and defense flows with offensive anddefensive behaviors and/or vice-versa and perform further semanticanalysis.

In similar ways with hazard flow analysis the system may perform threatand/or foe analysis.

The system adjusts the insurance premiums for artifacts and/or furtherowners (permanent and/or temporary) causing hazards and/or furtherdamage.

The system monitors locations and determine hazardous handlingconditions in regards with the items being handled. In some examples,the system determines that particular semantic identities have not beenhandled according with the established and/or inferred (handling)protocols (e.g. established by routes and/or rules) for the particularshipment and/or unloading location and/or endpoint and thus may furtherfactorize and/or index particular semantic attributes associated withthe handling/handlers semantic identity, routing, environment at thelocation (endpoint) and/or further circumstances.

It is to be understood that the handling protocols may be composable. Assuch, there may be rules for handling at a particular endpoint, rulesfor handling for particular environmental conditions, rules for handlingfor particular semantic identities (including handler and/or handled)and/or other circumstantial rules. Based on the determined semanticidentities and/or circumstances the system infer, factorize and/orcomposes the rules to be applied while preserving the coherency.

In some examples, the system detects that an item has been handledincorrectly, by using the wrong (e.g. not likeable for the handlinggoal) tools/artifacts/endpoints, using hostile behaviors and/or otherhazardous circumstances for the particular semantic profile. In someexamples, the handling goals may comprise goals on semantic attributessuch as “fragile” (e.g. the goal would be to not perform any activitywhich may be high entropic to fragile handling or handling with care orsimilar). In further examples, the fragility and/or other semanticattributes is/are associated with a portion of the item and/or furthermapped endpoints and thus the system may infer the conditions, hazardsand/or further manipulation and/or counter measure based on theparticular portion and/or endpoints.

The system may detect hazardous handling circumstances (e.g. smashing,opening, stealing etc.) based on semantic inference; in some examples,inferences from a camera and/or further wearables are used. They maysense that the artifact has been dissociated or dropped (e.g. by aperson, post, from a truck etc.), shaken, tampered with etc.; in someexamples such inferences are based on the outgoing, departure entropicorientation (having high drift from moving together and/or beingcontained), fall detection, factorized inferences, diffusion and/ordistance indexing in relation with the item and/or the carrier\ all ofwhich may comprise semantic time, orientation, damping/hysteresis of(projected) activity, movement, speed and/or acceleration and so on.

The system may automatically generate insurance claims comprisingexplanations (on explanatory assigned ui/storage areas/controls/fields)based on the inferences in the observing views and further when there islittle shift and/or drift from the circumstances instructed to handle(e.g. file a claim when the owner is in a hostile environment, file aclaim when the post crashed, do not file a claim if user says so etc.).Further, the insurance claims may be based on semantic time clausesand/or further budgets associated with provider services, deductibles,coverages, repair and/or medical clauses and/or further expenses.

The system may be biased with likeability of collapsing artifacts and/orinvalidating uncertainty in relation with particular artifacts atparticular semantic times.

With passing of (semantic) time, the system may affirmative indexlikeability (e.g. positively increase) and/or non-affirmativelyfactorize dissatisfaction (e.g. decay) for semantic artifacts associatedwith increased stress and/or dissatisfaction at the time of theexperience. This may happen when the experiences generated affirmativeconsequences, no non-affirmative consequences or non-affirmativeconsequences which can decay sufficiently in time.

The system may infer deception from cloaking and/or distortingparticular artifacts/opinions with high entropic artifacts/opinions inorder to achieve particular goals.

We expressed that the system may focus on particular semantic identitiessuch as “chair by the window”. As such, the system may look for theintermediate and/or anchor point associated with the window and furtherinfers the composite semantic identity; if the system is unable tolocate the anchor, infer, have access and/or collapse the compositesemantic identity then it remains in superposition, unidentified and/orunexplained.

It is to be observed that the system infers semantic identities based onlocalization, inference and/or further composition with the proximal,container/contained endpoints and/or associated attributes. In someexamples, the system infers the semantic identity of “chair by thewindow” and later on “the chair by the table”. It is to be observed thatboth semantic identities may refer to the same object and further theybe both valid and/or the second may invalidate the first (e.g. becausethe chair is by the table but is not by the window anymore). The systemperforms distortion reduction in order to correctly infer the semanticidentities. The system may associate such (temporary) semanticidentities with being associated with required artifacts for startingactivities (e.g. credentials, wallets, budgets, keys, fobs etc.).

The system infers whether at least one observer has been inferring thefirst (e.g. “chair by the window”) and/or the second semantic identity(“chair by the table”). In some examples, such inferences are based ondirect observations, UPIs, localization, behavior analysis and/orchallenges to/from the observers. Further, the system may use thesemantic profiles, semantic trails and/or further artifacts associatedwith the observer and/or further semantic analysis in order determinethe coherent and/or less confused narratives for the observer. In someexamples, the system addresses the observer with the second semanticidentity in the narrative if it determines that the observer has beeninferred the second semantic identity and/or with the first semanticidentity otherwise. It is to be observed that the system may composeand/or use any number of semantic identities if the user/observerconfusion is high and/or coherency is low (e.g. do you like the whitechair by the table which was by the window?).

The system and/or an observer may compose a semantic identity byanchoring it to artifacts within a semantic field and/or view and/orfurther semantic times (e.g. the chair by the window); it is to beobserved that the semantic time may be implicit based on circumstancesor more explicit. Further, when challenging a collaborator and/or otherobserver with the semantic identity the system may use other semanticidentities to identify the same artifact in order to coherently collapseand/or reduce confusion at the collaborator. In an example, if thesystem knows that S2P3 didn't observe (e.g. because observing field ofviews and/or challenges including of collaborators didn't comprise theanchor point and/or the semantic identity), infer and/or collapse the(current/targeted) semantic identity then, it may use (alternatively, orin addition) another semantic identity which may factorize and/orcoherently collapse at the collaborator (e.g. the chair by the fireplace(before you left) (two days ago)). Analogously, the system may use thesame techniques when it projects that the collaborator may haveforgotten a particular semantic identity and thus, when communicatingwith the collaborator, it specifies an alternate semantic identity forthe same artifact/object comprising another anchor endpoint and/orsemantic time in order to replace and/or reinforce the semantic identity(e.g. the chair by the window which was by the fireplace (two daysago)).

The system orients augmentation capabilities and/or further fields inorder to optimally augment the observer and/or user (e.g. orients soundfields, pressure, electromagnetic etc.). Further, the system uses ad-hocsemantic coupling for augmentation.

In further examples, the devices position themselves and/or arepositioned by a/the robotic/post arm in order to meet accessibilityinferences and/or further semantic profiles of particular users and/orartifacts.

In further examples, the assist capabilities may include helping usersto position their (mobile) devices for being accessible, read and/oridentified (e.g. position a user device close to a RF/ID proximityreader etc). Other examples may include position of cameras or readersfor (semantic) identifying the user (e.g. via biometrics, device etc).

The positioning, transition and/or movement of the artifacts take inconsideration diffusion, access control and coherency. Thus, asartifacts are indicated, positioned and/or moved around the physicaland/or virtual environment the system may allow and/or disallowparticular positionings, routes, locations, support, anchors and/orinteractions based on semantic inference. Thus, the system may ensurebelievability, coherency, feasibility (e.g. of moving and/or supportingin the environment) and/or further factorizations.

The ad-hoc semantic coupling may be used to broadcast information torelevant parties in various environments. In some examples, the systemlocalizes artifacts which may be exposed and/or projected to be exposedto hazards and thus, augments them with information on the hazardsand/or further explanations of required activities in order to escapethe hazards. In other examples, the system identifies devices and/orusers which are localized within a vehicle, deck, ferry boat, airplane,train, bus and/or other transportation modality vehicle and inform themon the required activities (e.g. board, deboard, watch your step, maybesalute the crew etc.). Further examples may comprisehome/restaurant/social venues and/or other environments.

It is to be observed that a device may be detected as having a semanticidentity and/or being attached, in possession and/or contained within asemantic identity. Further such semantic identities may compose fordecreasing confusion.

In further examples, the system uses semantic posts for(electro/magnetic) charging of energy provider modules (e.g. batteries,capacitors, chemical cells etc.). It is to be understood that the postsmay be dispatched at semantic times to charge particular modules and/ordevices by either wired and/or wireless (ad-hoc) (semantic) coupling.The dispatch may comprise carrying particular charging units which maybe composable; further, it may be based on energy budget projectedinferences (e.g. for ensuring augmentation, carrier, counter measureand/or other capabilities). In further examples, the system generatesaffirmative resonance and/or orientation for particular (mesh) endpointsand/or links for optimal electromagnetic charging.

The system may use forget inferences and/or further challenges inrapport with observer in order to infer whether the observer has beenforgotten a semantic identity and/or the confusion is high and/orcoherency is low.

The system may determine that at least one user may be associated with afirst observing view and/or observing entity at a first time and with asecond observing view and/or observing entity at the second time;further, the system may project whether the user has observed and/orforgotten between the first time and the second time particular semanticinferences associated with the observing views and/or further semanticaugmentation. The system may project lowering confusion and/orincreasing coherency towards the user augmentation in rapport with theuser/s, (its/their) observing entities, semantic identities and/orfurther semantic profiles.

The system may determine whether a user/post has been inferringparticular artifacts by observing expressed opinions, challenges,stimulation and/or excitement and/or high drift from intrinsic and/orleader behaviors.

At least two observing entities may be associated with the same userand/or different users. Further, the observing entities may beassociated with (semantic) fields (of view) and/or semantic views.

The system may infer and/or use semantic profiles for observingentities. The semantic profiles may be and/or comprise hierarchicalartifacts associated with a user and/or (its) observing entities and/orfurther semantic identities. It is to be understood that a user may be aperson, post and/or other devices/artifacts potentially capable ofreceiving and/or processing semantic augmentation.

During inference the system may infer assist type capabilitiesassociated with artifacts, views and/or domains which mitigates focusingand/or using budgets on such artifacts. In some examples, a deviceand/or post has a capability advertised as “I can support and/or assistwith water and bring it on challenge request or when thirsty” and thusthe system may use such feature and/or artifact to save budgets and/orfocus less on finding a preferred water supply/supplier; however, if thewater is not likeable the system may refactorize the support/assisthelper flux.

The semantic devices, posts and/or credentials position themselves forallowing identification, authentication and/or access so it can allowartifacts to transition, diffuse and/or access; these may be alsopublished as support and/or assist capabilities. In further examples,the semantic devices are challenged and/or instructed by theauthenticator and/or user to position and/or configure themselves forsuch identification, authentication and/or access. In some examples, theuser and/or authenticator device (e.g. another semantic device, display,post, lock etc.) indicates that it wants the device to go to a locationand/or endpoint in order to be authenticated (the device and/or theuser). While the semantic device may position itself, it may beunderstood that alternatively and/or in addition may be gripped and/orpositioned by a semantic post. The device may be instructed and/orenable/disable/adjust capabilities in order to be authenticated and/oronce at the endpoint. It is to be understood that the indications to goto a location and/or endpoint may be based on absolute and/or relativechallenges, endpoints, coordinates and/o semantic identities (e.g. comecloser 5 feet, go towards the fireplace by the window, position by theopen window on the left, position at entrance of the conferencecenter/room etc.); further, they may be based on UPIs wherein indicatesthe trajectory and/or location. The device may be also instructed toposition itself in a semantic time (e.g. at the entrance of theconference room by lunch, before dawn, before John arrives etc.).

The positioning, transition and/or movement of the artifacts take inconsideration diffusion, access control and coherency. Thus, asartifacts are indicated, positioned and/or moved around the physicaland/or virtual environment the system may allow and/or disallowparticular positionings, routes, locations, support, anchors and/orinteractions based on semantic inference. Thus, the system may ensurebelievability, coherency, feasibility (e.g. of moving and/or supportingin the environment) and/or further factorizations.

We mentioned that the system may adjust and/or index torque, power,further (rotational) speed, load orientation and/or positioning based onincline, elevation, location, hazards, environment, weather, slippage,lateral/forward acceleration and/or further goals etc. Further, thesystem may use gear/clutch assist capabilities in order to manage suchparameters. In further examples the system manages and/or adjusts suchcapabilities and/or parameters in order to realize positioning goalsand/or published support/assist goals within the required semantic time.

We mentioned that the system may allow transitioning and/or diffusionbased on capabilities of artifacts and/or counter-measure. In someexamples, the system may allow a semantic post carrying an approved fireextinguisher in a hazardous area encompassing explosive material whilenot allowing an expired fire extinguisher for such purpose. In otherexamples, the system may allow the post carrying the expired fireextinguisher if evidence is presented that the use the expired fireextinguisher do not further increases hazard (e.g. the post sends and/orredirects to an article and/or credential attesting is potentialcapability, the system instructs the post to spray a small hazardousarea which may not diffuse to the main hazardous area and furthermeasure the effects etc.).

The system may transition/allow/instruct artifacts to endpoints and/orfurther artifacts (e.g. fluxes, articles, links etc.) in order to allowthe artifact to transition and/or achieve being well-informed fromill-informed. As such, the artifacts may learn based on endpointcircumstances, conditions, inferences, routes and/or further artifactsat the new endpoint.

The system may perform semantic gating and/or access control based onwhether artifacts are well informed and/or ill informed. As such, thesystem may allow semantic devices and/or associated artifacts inhazardous areas and/or endpoints if they are well-informed in regardswith the conditions, indicators, factors, capabilities and/or furtherinferences at the endpoint and/or location and may not-allow, warnand/or block (e.g. artifacts, artifact/endpointusers/supervisors/owners/groups, resonant/non-resonant groups etc.) ifthey are ill-informed. In further examples, the system uses challengeresponse for determining whether an artifact is well informed and/or illinformed; further, the system may augment the artifacts so it maytransition from ill-informed to well-informed. It is to be understoodthat the hazard of transitioning the link and/or to a (location)endpoint may be based on whether the artifact may be affected by thehazard of transitioning (e.g. due to hazard of the link or at theendpoint) and/or may pose a hazard to the link and/or to endpointartifacts, environment, diffusions and/or further circumstances.

The system may augment an artifact in order to allow transitioning fromill-informed to well-informed.

In further examples, the system projects the stream of information tothe most relevant, effective and/or available devices associated withthe user.

In some examples, the system uses ad-hoc semantic coupling and/ortransition of augmentation from one device to another in order toaffirmatively factorize well-informed/ill-informed indicators. In someexamples, the system transitions augmentation from audio to video and/ortactile in order to factorize the well-informed indicators and/orsimilar for the user/supervisor. In further examples, the systemtransitions (augmentation) leadership from one artifact to another inorder to factorize such indicators.

The system may optimize budgets when moving items from one endpoint toanother using semantic capabilities.

The system may generate conditions at endpoints which are likeable byparticular semantic identities, semantic groups and/or generalizedaudiences. Such likeability factors may be based on further semanticprofiles. The system may adjust likeability conditions at endpoints inorder to route, direct, disperse, manage capacity/demand and/or otheractivities.

In order to bias, influence, and/or affirmatively factorize likeabilityof a targeted semantic identity at a collaborator (or semantic groupsthereof) in a semantic view the system may present to the collaboratoralternate semantic identities which are similarly affirmativelyfactorized on a first leadership attribute while being slightly lessaffirmative factorized on a second leadership attribute in rapport withthe targeted semantic identity. Such techniques may (project to) inducechanging anchor points in the collaborator and/or they may determineanchor point distortion.

The system and/or collaborator may counter-bias, use distortion and/orbudget conditioning in order to counter such techniques and preserve thequality of inferences (e.g. through coherency, low confusion etc.).While we exemplified affirmative resonance distortion it is to beunderstood that by H/ENT non-affirmative resonance distortion can beused (e.g. wherein semantic identities are distorted by slightly morefactorized non-affirmative options). The system uses such techniques toeliminate, condition distorted learning (e.g. intervals, semantic trailsetc.) and/or optimize budgets (e.g. by optimizing overshoot/undershoot,overspend/underspend intervals).

The system may perform distortion reduction by projecting whether suchtactics are deceptive and/or non-affirmative with the goals.

The system may detect deception by challenging the deceptive partyand/or flux network.

In some examples, challenges comprise and/or induce projected confusionfor the deceptive party. Further, the system may challenge the deceptiveparty to respond with activities, semantic identities, semantic trailsand/or further narratives associated with projected deceptive artifacts.If such artifacts are entropic and have high drift between various(semantic) times then the system may affirmatively factorize deception(towards the user goal to reduce distortion).

We mentioned that the system uses “earlier” and “later” type inferences.When projecting in the past (e.g. opposite and/or H/ENT to the future)the system may infer that the “earlier” from present (semantic) timetype inferences are associated with shorter semantic trails as opposedto “later” type inferences associated with larger semantic trails. Thus,the system is biased to infer that the time passed faster for inferencescomprised in and/or associated with shorter semantic trails and passedslower otherwise. When projecting in the future the system is biased toinfer that the time may pass slower when the number of activities in asemantic route are small and pass faster otherwise. Thus, in order tolocalize and/or collapse the present the system may use a superpositionof past and future projections and/or potentially counter bias thembased on semantic indicators, factors and/or budgets.

The system projects observed objects/people/modules/collaboratorsinferences and/or behaviors based on projected observing views of suchartifacts. The observing views may be further based on (semantic) fieldof views and/or further semantic views.

Obturation may be inferred by the system through “earlier” vs “later”inferences. As such, the “earlier” artifacts may obturate and/or distortthe “later” if they are in the same observing view. Further, the systemmay forget the “later” artifacts when they are obturated and are atleast borderline (affirmatively or non-affirmatively) resonant with the“earlier” artifacts. Collaborators/foes may induce obturations in thesystem's observing view and/or semantic view. The system may applydistortion reduction in order to counter bias the distortion, borderlinedeception, forgetful inferences and/or obturation introduced by foes;further, it may adjust the anchors and observing views and/or increasethe semantic spread.

Obturation may be inferred by the system through “earlier” vs “later”inferences. As such, the “earlier” artifacts may obturate and/or distortthe “later” if they are in the same observing view. The system mayforget and/or (non-affirmatively) factorize the distraction of the“later” artifacts when they are obturated and/or distorted; further,borderline resonance of the “earlier” and the “later” artifacts (and/orsemantic groups thereof) may further factorize forget/invalidate and/ordistraction of the “later” artifacts.

In some examples, an “earlier” object/vehicle A obturates a “later”object/vehicle B in the far field; because the objects semanticidentities seem to be less entropic (and/or have similar trajectories,behaviors, attributes and/or induce similar resonant sentiments) thesystem may infer affirmative resonance between objects and/or a cohesivesemantic group and thus, assigns attention or resources to the “earlier”as a leader and the group's (semantic identity) composed fieldenvelope/signature.

In other examples, the obturation comprises a smaller (particular) sizeobject A obturating another larger object B. The system may infer moreprojections (artifacts) and/or assign more attention or resources toobject A as it may potentially become more easily distorted, form morecontainment groupings and/or (composite) semantic identities in theobserving view than the larger object B.

Collaborators/foes may induce obturations in the system's observing viewand/or semantic view. The system may apply distortion reduction in orderto counter bias the distortion, borderline deception, forgetfulinferences and/or obturation introduced by foes; further, it may adjustthe anchors and observing views and/or increase the semantic spread.

The system performs semantic network model automation in which itchallenges the user to specify the meaning of various locations, mappedendpoints and/or oriented links in various circumstances and/or semantictimes.

The system may determine, collapse and/or assign trajectories (semantic)identification by leadership inference on sensing while transitioningthe trajectory and its artifacts.

The system may use semantic orientation and/or further leadershipinference to identify trajectories, trails and/or tracks. In someexamples, the trajectories are associated with sport tracks, lanesand/or runs.

In some examples, the system fusions (flux) information associated withan endpoint and/or location. The system infers coherent narratives basedon fusion of such information at semantic times. A user may specify,assign and/or instruct the system on its interests based on and/orcomprising trajectories, areas, endpoints and/or further semantic timesand thus the system performs semantic augmentation based on suchinterests and further semantic analysis.

A user may specify trajectories, areas and/or endpoints from which topublish/ingest notifications as those are inferred/published, based onsemantic artifacts and/or semantic gating. The user may specifyparticular semantic identities (and/or associated fluxes/channels)and/or other circumstances and the system may use similarity in semanticorientation, low drift and/or shift to identify such identities and/orcircumstances. The system may use ad-hoc semantic coupling to ingestand/or connect with semantic identities, fluxes, themes and/or semanticsartifacts (which publish information) at the targeted trajectories,areas and/or endpoints.

The system/user may be more specific and/or enhance/collapse theinterest space-time localization by considering/specifying thecircumstances at artifacts of interest which may have low drift and/orshift from interest semantic identities, conditions and/or furthercircumstances (e.g. notify and/or connect on semantics and/or fluxeswhich go through route/endpoint A in similar conditions with what Iexperience/d, notify and/or connect me with all experienced bike riderswhich pass and/or use A, notify me on the slide semantics published bysimilar experience bike riders in freezing conditions at A). The systemmay further infer and/or assign likeability, resonance and/or furtherfactors to coupled fluxes/channels. In some examples, based onlikeability, affirmative resonance, friendliness and/or furtheraffirmative factors the system may decide to keep connected and/orconnect at semantic times with such fluxes/channels. In furtherexamples, by H/ENT, based on non/low (non-affirmative) likeability,resonance and/or further factors the system may disconnect and/or expiresuch fluxes/channels.

The system may infer and/or project the availability of semanticidentities, capabilities and/or artifacts at particular semantic timesand further based on semantic profiles. In some examples, the semanticidentities and/or further artifacts (e.g. endpoints, links) areassociated with parking endpoints (e.g. clean parking spot, parking spotno 3, the parking spot by the store, unoccupied parking spot by thestore, Jane's parking spot etc.); further, they may be associated withdriving and/or traffic areas/lanes and/or associated artifacts. In otherexamples, they are associated with chairs, tables and/or seats in anevent venue or facility (e.g. conference room, sport venue, exercisevenue, hair dresser, washing venue etc.). In general, the semanticidentities may be associated with mapped endpoints in differentenvironments and/or circumstances.

The endpoints contain and/or are contained in other endpoints (e.g. asemantic camera module “S2P2 camera” is comprised in S2P2, S2P2comprises “S2P2” camera, Jane's parking spot comprises Jane's(potential) (beetle) car etc.). The contained/containment may beentropic and/or superposed (e.g. not collapsed to one well definedsemantic identity endpoint); as such, Jane's parking spot may notcomprise and/or contain entirely Jane's (potential) (beetle) car becausethe car partially occupies another adjacent parking spot; further, thecar may not occupy entirely Jane's parking spot and thus the system mayinfer that “Jane's parking spot 81% comprises Jane's beetle looking car”and/or further “Jane's beetle is contained (maybe) 63%-67% in herparking spot and (maybe) 25% in John's). It is to be observed thatthough the system may consider the beetle car in superposition inregards to the parking view at a semantic time, in further examples, itmay collapse the superposition if the semantic identity of Jane's andJohn's parking spots/endpoints is collapsed, fusion-ed and/or redefined(e.g. DOE parking area, Jane's beetle is parked in DOE'S parking area).Similarly, the system may determine occupancy of traffic lanes,event/venue furniture (chair, tables etc.) and/or other artifacts (e.g.furniture, fireplace ledge etc.). The system may collapse Jane and John(parking) area/endpoints semantic identity if they are not highlyentropic in the observing view in relation with the beholder's/observerview goal. In further examples, the observing view see the DOE semanticidentity as highly entropic and/or likes one Doe and/or dislikes theother and thus it cannot accept/allow the (circumstantial e.g. parking)semantic identity to be collapsed and/or assign it to a/the highlyentropic semantic group. In some examples, we mentioned that the systemmay be challenged to describe and/or explain orientations within thesemantic field and/or further observations/opinions (e.g. “where's yourhead at”). Further challenges to explain and/or describe anchors may beused (e.g. what is at your most right (endpoint)? How long did it taketo reach it? How is the polarization distortion? Is it friend or foe?,what is on the left side of the chair by the windows?, what is behindthe chair? what is at 11 orientation?, which magazine is the nurseholding? Health Affairs? etc.). Thus, the system may learn and/orcalibrate inferences and/or sensing based on semantic fusion.

Users may instruct the system to observe, infer and/or learn atparticular semantic times. Analogously, the users instruct the system tonot observe, infer and/or learn at particular semantic times.

The system determines the leadership which determined readiness and/orsuccess and/or, leadership which determines non-readiness and ornon-success/insuccess.

The system may gate/publish artifacts and/or further explanationscomprising summary of learning, capabilities and/or further semantictimes. In further examples, collaborators/fluxes may challenge thesystem and/or the user to specify what (capabilities) it did learnwithin a semantic time. The system may use explanations comprisingleadership artifacts, inferences and/or narratives.

In some examples, users and/or collaborators may select various modelsand the system performs fusion of the models; during fusion the systemmay reduce confusion and/or increase coherence through challenges to thecollaborator and/or the user/owner/supervisor.

In some examples, the system performs fusion to be applied and/ortransferred to semantic posts, modules and/or units.

While published models may be associated with posts alternatively, or inaddition they may be associated with endpoints, venues, events and/orfurther semantic times. As such, the system, collaborator and/or usermay select to apply particular models to other endpoints, venues and/orevents based on particular (resonance) goals and/or factorizations.

In some examples, John likes the explanations and/or/of capabilitiespublished for a particular football venue and applies its model to adesired baseball venue. Further, John may apply the capabilities fromanother baseball venue and applies it to the desired baseball venue.While the capabilities may be applied overall to the desired venue it isto be understood that they may be applied only to specific semanticprofiles and/or to the user (John) based on access control and/orfurther challenges (e.g. to the user). While (sports) venues have beenexemplified it is to be understood that the venues may be associatedand/or substituted with other types of containers, vehicles, posts,carriers, facilities, warehouses, stores, houses, rooms and/or modeledartifacts.

While performing inference, in order to reduce the semanticshift/drift/orientation and/or achieve semantic resonance with thedesired/likeable (fusioned) models, the system may look to map, groupand/or infer resonance between the existing capabilities at endpointand/or existing capabilities at the specified model endpoint.

The system infers non (affirmative)-resonance and/or lack ofcapabilities to the desired venue/artifact when (projects) applying themodel (e.g. the system infers that John's baseball venue doesn't havecountermeasures and/or spaces are too small). Thus, the system maychallenge the user and/or fluxes (whether associated with the specifiedmodels or not) to demand/acquire/transfer/transport such capabilitiesand/or project the flows in order to counter act such shortcomings andthus inducing (e.g. at the users, visitors, collaborators etc.) thedesired (affirmative) resonant capabilities.

While a user may apply particular models to selected artifacts, inaddition, it may also specify its own desires, rules, routes and/orchallenges.

A user may like a semantic model to be applied over the other based onthe explanation of capabilities, learnings and/or future projections ofthe missions or goals. In some examples, John may prefer the beetlelooking car if he travels and/or has a trajectory to a venue or goalprojecting animated characters resonance and thus creating (affirmative)resonance between John, goals and the selected model; further, the(affirmative) resonance may diffuse to the artifacts at the venue and/orendpoint (e.g. John is more likeable and/or affirmative resonant at thevenue, endpoint etc. because he is grouped/identified with a beetle).

We mentioned that the system may use semantic profiles to interpretinformation and/or learn. As such, the semantic profiles may behierarchical where more general semantic profiles associated with moregeneral semantic identities have a larger resonant interval while themore focused and/or specific semantic identities have more specificresonant semantic intervals. The system may associate the collapseand/or compose of semantic identities with the collapse, reductionand/or more specific localization of the specific (hierarchical)semantic profiles intervals within a semantic time.

The system may gate/publish artifacts and/or further explanationscomprising summary of learnings within a semantic time. In furtherexamples, collaborators/fluxes may challenge the system and/or furtherthe user to specify what did it learn within a semantic time. The systemmay use explanations comprising leadership artifacts, inferences and/ornarratives.

We expressed that the system may use time management to project theoptimal circumstances and/or (semantic) (time) interval for activitiesand/or use of budgets.

In some examples, in the application the term “doubt” is used. It is tobe understand that the doubt may be associated with increasedsuperposition and/or confusion factors. The doubt, superposition and/orconfusion factorization may be affirmative/non-affirmative towards thesystem's goals. Further, we mentioned that the doubt may cause largerdamping and/or hysteresis; in order to decrease doubt the system maytarget to reduce damping and/or hysteresis and thus further increasingsemantic field localization.

In some examples, the system counts the number of transitions and/orfurther activities of an artifact to an endpoint and further infers anumber of (activity) circuits and/or shapes within an interval if theroute between entering and/or starting (the artifacts and/or activity)and exiting and/or ending the activity has little drift from oneanother. The system may infer closed and/or lapped activities, semanticshapes and/or further associated (semantic) times, routes and/orcharacteristics. Further, based on such inferences, the system mayfurther infer particular semantic attributes and/or further rules forthe enclosed area and/or shape.

The system projects particular shapes and/or associated semantics basedon diffusion capability (of the semantics), composition, saturation,damping, hysteresis, resonance and/or further semantic analysis. In someexamples, conditions and/or waves at endpoints and/or locations arewithin hysteresis and/or damping interval and thus a perturbation(incoming and/or outgoing) and/or change of circumstances at theendpoint may determine further superposition, diffusion and/oroscillatory orientation of the particular superposed and/or dampedactivities. In some examples, recurrent/closed/lap activities maydetermine ongoing oscillation and/or further resonance which canassociate the lapped shapes, artifacts and/or activities in the semanticview.

It is to be understood that the term “system” used in this disclosuremay take various embodiments based on the contexts as disclosed. In someexamples, “system” may represent, but not limited to, a post, a semanticcloud, a composable system, a semantic engine, a semantic networkedsystem, a semantic memory, a semantic unit, chip, modulator, controller,mesh, sensor, I/O device, display, actuator, electronic block,component, semantic computer, mobile device, robotic device and anycombination thereof.

Further, any functionality implemented in hardware may be implemented insoftware and vice-versa. Also, functionalities implemented in hardwaremay be implemented by a variety of hardware components, devices,computers, networks, clouds and configurations.

While the preferred embodiment of the invention has been illustrated anddescribed, as noted above, many changes can be made without departingfrom the spirit and scope of the invention. Accordingly, the scope ofthe invention is not limited by the disclosure of the preferredembodiment. Instead, the invention should be determined entirely byreference to the claims that follow.

I claim:
 1. A semantic augmentation system, comprising: a sensor; acomputing system and a memory in communication with the computingsystem, the memory storing a plurality of endpoints; the computingsystem being in communication with the sensor to receive a first sensorinput at a first time and a second sensor input at a second time, eachof the first sensor and the second sensor being arranged to detect thepresence of an object; the computing system further being configured todetermine that a user possesses the object, based on at least one of thefirst sensor input or the second sensor input; the computing systemfurther being configured to perform semantic augmentation associatedwith the user by inferring and conveying a narrative indicative of aprojected activity by the user; the narrative comprising a firstcomposed semantic identity inferred at the first time and referring tothe object and being based on the first sensor input at the first time;the narrative further comprising a second composed semantic identityinferred at the second time and referring to the object and being basedon the second sensor input at the second time.
 2. The semanticaugmentation system of claim 1, wherein the computing system isconfigured to invalidate the first composed semantic identity afterinferring the second composed semantic identity.
 3. The semanticaugmentation system of claim 1, wherein the computing system is furtherconfigured to: determine that the user is no longer present at one ofthe plurality of endpoints; and infer an activity readiness based on thedetermination that the object is in possession of the user and thedetermination that the user is no longer present at the one of theplurality of endpoints.
 4. The semantic augmentation system of claim 1,wherein the computing system is further configured to invalidate thesecond composed semantic identity based on an inference that the secondcomposed semantic identity is associated with the user oriented in anobserving view.
 5. The semantic augmentation system of claim 1, whereinthe system infers the first composed semantic identity or the secondcomposed semantic identity based on a localization of the object at oneof the plurality of endpoints.
 6. The semantic augmentation system ofclaim 1, wherein the computing system is further configured to receiveone or more user pointer indicators associated with the user from a userpointer sensor, and further wherein the computing system is configuredto infer the first composed semantic identity or the second composedsemantic identity based on the one or more received user pointerindicators.
 7. The semantic augmentation system of claim 1, wherein thesensor is at least one wearable sensor.
 8. The semantic augmentationsystem of claim 1, wherein the object is localized into a storeenvironment.
 9. The semantic augmentation system of claim 1, wherein theobject is localized into a home environment.
 10. The semanticaugmentation system of claim 1, wherein the object is localized into aphysical-virtual fusion healthcare environment.
 11. The semanticaugmentation system of claim 1, wherein the object is localized into aphysical-virtual fusion sporting environment.
 12. The semanticaugmentation system of claim 1, wherein the sensor is configured toadjust an observing field of view based on an inference of the user irisorientation.
 13. A semantic augmentation system, comprising: a sensor; acomputing system and a memory in communication with the computingsystem, the memory storing a plurality of endpoints; the computingsystem being in communication with the sensor to receive a first sensorinput at a first time and a second sensor input at a second time, atleast one of the first sensor or the second sensor being arranged todetect the presence of an object within a field of view of the firstsensor or the second sensor; the computing system further beingconfigured to project an activity at an endpoint for a user and furtherinfer that the user needs to carry the detected object to perform theactivity; the computing system further being configured to performsemantic augmentation associated with the user by inferring a narrative;the narrative comprising a first composed semantic identity inferred atthe first time and referring to the object and being based on the firstsensor input at the first time; the narrative further comprising asecond composed semantic identity inferred at the second time andreferring to the object and being based on the second sensor input atthe second time.
 14. The semantic augmentation system of claim 13,wherein the computing system is configured to invalidate the firstcomposed semantic identity after inferring the second composed semanticidentity.
 15. The semantic augmentation system of claim 13, wherein thecomputing system is further configured to: determine that the object isin possession of the user; determine that the user is no longer presentat one of the plurality of endpoints; infer the narrative wherein thenarrative further comprises a third composed semantic identity; andinfer an activity readiness based on the determination that the objectis in possession of the user, the third composed semantic identity, andthe determination that the user is no longer present at the one of theplurality of endpoints.
 16. The semantic augmentation system of claim13, wherein the computing system is further configured to invalidate thesecond composed semantic identity based on an inference that the secondcomposed semantic identity is associated with the user oriented in anobserving view.
 17. The semantic augmentation system of claim 13,wherein the system infers the first composed semantic identity or thesecond composed semantic identity based on a localization of the objectat one of the plurality of endpoints.
 18. The semantic augmentationsystem of claim 13, wherein the computing system is further configuredto receive one or more user pointer indicators associated with the userfrom a user pointer sensor, and further wherein the computing system isconfigured to infer the first composed semantic identity or the secondcomposed semantic identity based on the one or more received userpointer indicators.
 19. The semantic augmentation system of claim 13,wherein the object is localized into a physical-virtual fusion sportingenvironment.
 20. The semantic augmentation system of claim 13, whereinthe sensor is configured to adjust an observing field of view based onan inference of the user iris orientation.
 21. The semantic augmentationsystem of claim 1, wherein narrative further comprises a third composedsemantic identity and wherein the computing system is configured toinfer an activity readiness based on the third composed semanticidentity.